{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## GeostatsPy: Cell-based Declustering with Basic Univariate Statistics and Distribution Representativity for Subsurface Data Analytics in Python\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PGE 383 Exercise: Basic Univariate Summary Statistics and Data Distribution Representativity Plotting in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic univariate statistics and distribution representativity. This should help you get started data declustering to address spatial sampling bias.\n",
    "\n",
    "#### Geostatistical Sampling Representativity\n",
    "\n",
    "In general, we should assume that all spatial data that we work with is biased.\n",
    "\n",
    "##### Source of Spatial Sampling Bias\n",
    "\n",
    "Data is collected to answer questions:\n",
    "* how far does the contaminant plume extend? – sample peripheries\n",
    "* where is the fault? – drill based on seismic interpretation\n",
    "* what is the highest mineral grade? – sample the best part\n",
    "* who far does the reservoir extend? – offset drilling\n",
    "and to maximize NPV directly:\n",
    "* maximize production rates\n",
    "\n",
    "**Random Sampling**: when every item in the population has a equal chance of being chosen. Selection of every item is independent of every other selection.\n",
    "Is random sampling sufficient for subsurface?  Is it available?\n",
    "* it is not usually available, would not be economic\n",
    "* data is collected answer questions\n",
    "    * how large is the reservoir, what is the thickest part of the reservoir \n",
    "* and wells are located to maximize future production\n",
    "    * dual purpose appraisal and injection / production wells!\n",
    "\n",
    "**Regular Sampling**: when samples are taken at regular intervals (equally spaced).  \n",
    "* less reliable than random sampling.\n",
    "* Warning: may resonate with some unsuspected environmental variable.\n",
    "\n",
    "What do we have?\n",
    "* we usually have biased, opportunity sampling \n",
    "* we must account for bias (debiasing will be discussed later)\n",
    "\n",
    "So if we were designing sampling for representativity of the sample set and resulting sample statistics, by theory we have 2 options, random sampling and regular sampling.\n",
    "\n",
    "* What would happen if you proposed random sampling in the Gulf of Mexico at $150M per well?\n",
    "\n",
    "We should not change current sampling methods as they result in best economics, we should address sampling bias in the data.\n",
    "\n",
    "Never use raw spatial data without access sampling bias / correcting.\n",
    "\n",
    "##### Mitigating Sampling Bias\n",
    "\n",
    "In this demonstration we will take a biased spatial sample data set and apply declustering using **GeostatsPy** functionality.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data_biased.csv at https://git.io/fh0CW\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        \n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')         # suppress warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "from scipy import stats                   # summary statistics\n",
    "from statsmodels.stats.weightstats import DescrStatsW # any weighted statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"d:/PGE383\")             # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('sample_data_biased.csv')     # load our data table\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No error now! It worked, we loaded our file into our DataFrame called 'df'. But how do you really know that it worked? Visualizing the DataFrame would be useful and we already leard about these methods in this demo (https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     X    Y  Facies  Porosity       Perm\n",
      "0  100  900       1  0.115359   5.736104\n",
      "1  100  800       1  0.136425  17.211462\n",
      "2  100  600       1  0.135810  43.724752\n",
      "3  100  500       0  0.094414   1.609942\n",
      "4  100  100       0  0.113049  10.886001\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[0:5,:])                   # display first 4 samples in the table as a preview\n",
    "#df.head(n=13)                           # we could also use this command for a table preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 2 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>289.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>289.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>475.813149</td>\n",
       "      <td>529.692042</td>\n",
       "      <td>0.813149</td>\n",
       "      <td>0.134744</td>\n",
       "      <td>207.832368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>254.277530</td>\n",
       "      <td>300.895374</td>\n",
       "      <td>0.390468</td>\n",
       "      <td>0.037745</td>\n",
       "      <td>559.359350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058548</td>\n",
       "      <td>0.075819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106318</td>\n",
       "      <td>3.634086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>430.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.126167</td>\n",
       "      <td>14.908970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>670.000000</td>\n",
       "      <td>819.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154220</td>\n",
       "      <td>71.454424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>990.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228790</td>\n",
       "      <td>5308.842566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                X           Y      Facies    Porosity         Perm\n",
       "count  289.000000  289.000000  289.000000  289.000000   289.000000\n",
       "mean   475.813149  529.692042    0.813149    0.134744   207.832368\n",
       "std    254.277530  300.895374    0.390468    0.037745   559.359350\n",
       "min      0.000000    9.000000    0.000000    0.058548     0.075819\n",
       "25%    300.000000  269.000000    1.000000    0.106318     3.634086\n",
       "50%    430.000000  549.000000    1.000000    0.126167    14.908970\n",
       "75%    670.000000  819.000000    1.000000    0.154220    71.454424\n",
       "max    990.000000  999.000000    1.000000    0.228790  5308.842566"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specify the Area of Interest\n",
    "\n",
    "It is natural to set the x and y coordinate and feature ranges manually. e.g. do you want your color bar to go from 0.05887 to 0.24230 exactly? Also, let's pick a color map for display. I heard that plasma is known to be friendly to the color blind as the color and intensity vary together (hope I got that right, it was an interesting Twitter conversation started by Matt Hall from Agile if I recall correctly). We will assume a study area of 0 to 1,000m in x and y and omit any data outside this area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0               # range of x values\n",
    "ymin = 0.0; ymax = 1000.0               # range of y values\n",
    "pormin = 0.05; pormax = 0.25;           # range of porosity values\n",
    "cmap = plt.cm.inferno                   # color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing Tabular Data with Location Maps¶ \n",
    "Let's try out locmap. This is a reimplementation of GSLIB's locmap program that uses matplotlib. I hope you find it simpler than matplotlib, if you want to get more advanced and build custom plots lock at the source. If you improve it, send me the new code. Any help is appreciated. To see the parameters, just type the command name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.GSLIB.locmap(df, xcol, ycol, vcol, xmin, xmax, ymin, ymax, vmin, vmax, title, xlabel, ylabel, vlabel, cmap, fig_name)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can populate the plotting parameters and visualize the porosity data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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du9tavErDx29/QkNNG2p61sXT0Yf6Xk1xueHL7Jm/F3kOe3t7RowayrWkI+iN6UgpiU2+Dm7xDH38MStKL5FmY7EPhbwov9AKVsHPz4+fFv5OaF0XpkTsZanxMo+//ypDhw+ztWjllqtXr7J69Wr27duH2Wwu8XzvffYpdR4fxMdn9zLp2DYSWtZl+h/zixTRUJ749MuPeeSlvpzV7uWoeSvNHqrJvMW/V7jzKK9IKbl+OQLP26J/qrvXYdvm7UWa4+Znt9V9LXjtg7FkuoUSZd5P8x4B/PnXvCJbHYqFlEhpKvahkBflm6VgNYKDg/nyx+9tLUa5R0rJt198wMU9q+kQZOB4qpafdT58M30Bfn5+hU9wB06dOsWaPxfQw98BB+HI7v+2snl9Ux4eYs27t9JHrVbz1DOjeeqZ0bYWpVIihEClVWEym1CrbtXqSdOn4FPNp4CRls/u++9+yOa1/6HWu4NWj6ufirkLZpXos3tXCDUq4VE2a90jKIqBgoKN2bp1CynHVjDlQY9sx60jV+P54r1X+X5G8RI5mUwmPnn1Zd5rGIivsyUmf7DJxLs//UCbDh2pWrUoNV4U7hUeHvogm+f8R2PPVgghMJmNnNEd5uPnC878uWnTJjav3E+we1uEi+Wzm3Ajkjdfe5vf580qC9FBGjGbY8tmrXsEZStBoVIjpeTGjRukpBQ/l3p+6HQ6YmJiSsXkv2n5Hwxt4ZDLm7tFsAuxV06j0+mKNeeJEycIsRPZSgGAnVpNHz8XNq1bW2KZFUoHs9lMTEwM6enpNpXjhZeep+3DzdiVsp5DKTvYm76ZsZOeomPHjgWOW7Tgb7wdauX67Hq6VOHMyYtkZGQUMLJ0kdJY7EMhL4rFQKHScuzYMb798DVczImk6SVV6rbk7U++ueuoiJxkZGTw9afvcO7QNjydIE7vxLg3P6djp87FnlNKSX7ZjUuS8tiSuCz/5GXSXDmTmlU0Nm3cxE+ff42LSU2yMZ1293fjjffesUnyKbVazdvvv8Urr71MQkICfn5+RZJDIvOt+AM3P4NlgQSzoYzWujdQLAYKlZK4uDgmv/40H3dI57sBbsx4yJ1umsN88Pq4Es371SdvE5K2idnDHPjuIUemDDQw/bOXCAsLK/acvQY/zpKj6bl+SE9cS8UjqC5OTk7FmrNJkyZcyJTE6W7dtRlMZjbGpNCrX79iy6pQOpw+fZpp737OGJ9WjKrahpeqdyFl2ym+/+Irm8rl5OREUFBQkZWTIY8/QmxGWK7PbmJaNPUa1sbR0bGAkaWHzEqJrFgMSg9FMVColKxe/jcPhZgIcL9VlrhbPXcyIs8QEVG8QmVpaWlcOLyNh1u6Zt/NezhreKq1mX8WzS22rL163Y+2Xn9eXZnC0oOx/LAtie+PuPPWJ98Ve06NRsPbX3/Hhyeu8ceZyyw/d5lJB87zwLPjCA4OLva8CqXDH7PmcL9XCI5aS90FIQTdAxuwfd0mDIaKc/fbp09vuvZrwdW0/VyPO8v1lOPgFcWX33xWZjIIoUElvIt9KORF2UpQqJTEx0TR2k2dpz3QRRAfH09gYOBdz5mcnIy3syqPib+Kh5Yt4eHFllUIwVsfTiY0dCzHjh2jg68vE9u3R63OK//d0KJlSxau38TOnTvJyMhgavv2+Pr6Fj5QwerciIqmqWNuj3+VEDgKDRkZGRWmloVKpWLyV58RGhrKsWPH8PPzo127diX+7N4NUhowm6LLbL17AUUxUKiUtOzQjW3Tl3NfzVtteqOZU7GC1+vUufPAAvD39yc6w4HkdCNujre+OtvO62nZu3dJRaZWrVrUqlWrxPPkxNHRkd6lIJtC6dKuR1eOz1tP16BbWSgTM9IQro64uORNZV3escZnt8hICWYlH0FpomwlKFRKOnfuTLx7M37eGU/oDR2HryTz2uoEHn/udRwcHIo1p0ql4rmJHzPxnwz2nE8mLCaD2TsTOZRamwEDB5XyGShUZoY8/hgX3QxsDT9NdFoix29cZX7kQV7/+N0SOZ3es0hj8Q+FPChllxUqLUajkQ3r17Fr00pc3D0ZNGQkjRs3LvG8Fy9e5J9Fc4i/EUHrTn0YOOhB7O3tCx94DxIREcGyJUuJiYyhc8/O9OjZs0zNzOUZnU7H8qX/cGDnLqpUDeKxJ4dTvXp1W4tlNYQQVilv3LJFVblz64vFHu/i9ZZSdvk2FMVAQUHBKuzbu5f3xr9NQ3UtXO1cCEu/jkM9d6b/PkNJZ3wPYjXFoLm/3L5paLHHu/n9qCgGt6FsJSgoKJQ6Uko+eetj+nl2oaFPXaq5BdLFvw2ZZ5NZv26drcVTqExICWZj8Q+FPCiKgUKJuXr1Klu2bOHChQu2FuWe4ezZs2zZsoXwEkRDWJOoqCi0GSqc7XLnYajrUoOt67bYSCrbcOXKFbZs2cLFixcL7Gc2mzly5Ahbt24lPj5vqWmFO6EoBqWNYs9TKDYmk4n3Xnud6wePUtvehauGdBxrV+ebX34qtoOfQsGkpaXx5vincNado463kb+ua6jWtDdvfTi5XJWzdnZ2Jt2Umac91aDDy9vfBhKVPUajkTcmvMnJPWdwM3uSqkoiuHEQU2f8mMcnJSoqinGjn0ebIHDCnsnGaB4dM5Rnnh9rI+krFkKpkliqKIqBQrH5c/58OHqOl0NaZbdtuX6RX77/gVffmmRDyQrHbDazedMmNq9cjEqlom33/kRcu8iFk4eoXqchIY1asXfrajJ0Olp37U16WgrHD27H178a/3viGerWrWsTuX/8+mN6+52mf3NXAEZJyQ8b17Bi+X089PCjNpEpP9zc3KjfsgFnT1yivldtAPQmPcfSz/HjiJdtLF3ZMO/3+VzeGU4H7x7ZbWePn2Dq91N5bdJrufq+Of51GulqUNW3CgBmaWbVrGW0bncfLVq0KFO5Kx4aVOoAWwtRqSg/txgKFY41S5bSr2runABdA2uxc/3GIs+RlpaGXq8vbdEK5YsP3mLPrLd40vci92uP882bI3E9+R1vtAolZvcM5r73OH0ddvOI7ymmf/w8STvfYWLbC3R3Wc8XEx/lv51Fq1Nf2hzes5m+TW/FuQshGNnBifXL5ttEnoL49JvPyayvZnn0ZrbE7mFV4jZe/niizZSqsuafxctp4NE0V1uIe0M2rNqUqy0uLo64KzFUdauS3aYSKpo612PpH3+XiawVGmnAbAgv9qGQF8VioFBsTEYjmtvM1yohilRx8Ny5c3zx1ttk3Igl02yiSfv2TPr4o2LXBrgbLl26RMSRLXzVxxchBH8dvMbnAzQEeWWiEiYuXk+hpo/k+y2hGFDh4qSndVVwtTNwX4gLdaoYmfDdB3Ts9G+Zx5wL8hZcstOoMBrLXxpdFxcXfv7tF27cuEFSUhLVq1evMBn9SgOTyYRKFP79MJlMqEXeEE61UJOhL3/va/lDIhRfgVJFsRgoFJseA/uzPSJ38aBD0ddp1alDgeMSEhKY9MxYhju6817D5nzSqCVVzl7knQkTrCluNkcOH6ZTgIm4NAPLj0Sx+UwMdXwEF6ON/LjmOhEperp2dmbqBC++esGTT5/xZvIGPbM3RrDlaCLO9ipcVMkkJyeXibw5qdukLXvOp+Vq+/tAKt37lZ9thNvx9fWlTp0695RSANBn4P2cTzyTqy0s6RKduuf+fvj5+WHn5UCcLiG7TUrJ6bSLDPzfA2Uia4XHbCr+oZAHxWJgZdLT04mPjy9yGdOKxKixz/DS3n2Ehx6njtaF68Z0wl3tmDbpzQLHrVmxgq5O7lRxseyTCyHoEhTM7jPHiYyMpEqVKgWOLyl+/v7Mv5jCyiNh9K+nYkBdyYBZaXRr7UKt2lr8E7VUD7ZH2KlwtxcYzGo6NHcmKk6HJjyS37dEoNMEW716XEpKCikpKQQEBGQ7Fr4y6RMmPDuUA1ejqetjZP91O5KdW/D148OtKotCweh0OhISEvD398/O0TD2hbEc2DuWQ6G7cTN6kqpJRhMgmTjp6zzjP5/yJeNHj6NKijeO2HNNRtOqT1s6depULHkSEhLQ6/X4+flV/kyKUioZDEsZRTGwEmazmW8nf8uGf9bjqnEhVaYx5qWnGfpE8RNxlDccHByYuXA+Bw4c4PyZs7SsVZOOHTsW6h0fde06VfO5qPrZ2RMbG2t1xaB+/fqcCb/B38Mc8HDUMG2vkRf/506H1m6ER2Zw6ZoGb3cVaekSg8mMnVbQoo6WC8LI830daVpDz+cbJHZ2dne1bmhoKPNmTeHi2WNUqxHCiKcn0LBhwzz90tPTmfzxm4Se3Imvm5qrCRpqhrQg6so5XFzdeGzUeJycXYgMv8pDQ5rSokWLyv/jX04xm8189dnXrF+xAWeVC+kqHc+/8iyPDn0UR0dH5i2ey8GDBzl7+iy16tSiQ4cO+X4/QkJC+GfzSrZu2UJcbBwvt29XLF+MuLg43nrlDSLPX0Or0iDctXzy3Rf5fs4qC0JoUWuCbC1GpUJRDKzEr9N/5fCyAwzw7YNKqDCajcz/Zg6B1QLp0qWLrcUrNYQQtGnThjZt2hR5TMsO7dm4fSdN/G55EhvMJsIydNSuXdsaYuZiz549jO4aSKpMIykpk//CjUwb7UZcmuRIqIEDZzPQ681U8VIjzZK0TMmy/3Q08JMcuWyibk1fqnjbk5aWhrOzc5HWvHTpEu++PIRXB0Dzbi6cvXaUL98axssf/EbLlq1y9f3yk0k0s9vGe8+5YDBKnvn2LNXCTzGpT33SjTpmLHiHal1GM3bcq9Z4eRTugmk/TWf/ssN09eyb/T2f8cVvVKlahU6dOqFSqYr8/XBwcKD/gAHFlkVKyfinx1EvwYcuVSyREPHpSbw65iX+2rgcV1fXYs9dnpFSj1l/1dZiVCoUHwMrseyPpbT2aZntfKRRabjPoxW///K7jSWzPV27dSMhwI+ll84Tk5bKpYQ4ppw5zpCxz5SJ86FGo0GqNATXqEX12vVQqVQs3pLMe9MjuBSWQmqaiW8XJXD+up7IOCPzN6ZwOiyTAxdM/LI+k5/XpGIwmu/qLn329K95Y5CgZYgrKpWgYXVnPnrMgVk/5a5bn5qayqUTOxjUxgUhBJuPJNI20MwTbezQJSfg52bHO3092LZqISkpKaX90ijcJcv+/IdGHi1yfc8bO7dkzvS5ZS7LhQsXMEalUcujanabl6M7dQhgw7r1ZS5P2SEVH4NSRrEYWAmT3oRGlfvldbN3Iy72uI0kKj+o1Wqmzv6N5cuWsXTtOlz9vXjh9Ze57777ymT9Ll268PQPdjzYzFI+2UMD5y8n8/sEN46FmXDWmujTUs2iDUnoMiWNqql4+xEHwqLg+b7OzNyUzJrDkl9//pbWHXrQvn37QrdPwi6coknv3NaFan4OJMbmDpdKSUnBx1WVrXScvZJKp2oqtGqBMcPioa5SCRr6Ca5cuVIqRaGKwvnz59mwajVqtZoefftw8cIFzh47Qa369ejbv1+pKHTnz59n7Yo1aLQa+g8aYLsyvkVESmn5njvl/p672LkSFnPmDqOsR3x8PK6qvFt0ripHbkTfKHN5ygwJogiRUCVBCNEXmAKogVlSysm3Pf8EcNO5KhV4Xkp5TAhRDZgHBABmYKaUckrWmMVAvawxHkCilLK5EEILzAJaYrlGz5NSfmHN87sdRTGwEtVqVeNGZCy+zj7ZbRfiL9Lpwc42lKr8YGdnx5ChQxkytOx9LlxdXXnp/e8Y98lrdKyaxrl4M58NdiAmwUxckpHQKBPdm9rRsaEdcckST2eYvzUDRy1sOZ7BpgMZjGpipkXqErZM/4vli+5j8g8zC6wa6BdQlctRodSscuuHOz7ZgL2TR65+/v7+RKU5kpRmwN1ZQ1U/R85HJxPsrsLZ1S27X2i82eq+GDeZPWMmm+b8QWe3AHRGAw9/9iXdq9ajRZXqHN1ykAXTf2X6H/Pw8/Mr9hozp81k+ay/qa+tiRSScfNXMHLCGB4f/ngpnknpIoSgas0g4iPi8HL0zm6/nHSJLo+X/XZho0aNuG6IxWg2oVHd+iyGGqMY2qVjmctTVghhh9BWLbxjsecXauBn4H7gOnBACLFSSnk6R7cwoKuUMkEI0Q+YCbQFjMBEKeVhIYQrcEgIsUlKeVpK+ViONb4FkrIePgrYSymbCCGcgNNCiD+llJetdpK3oWwlWIm3Pn6bfZkHORt7jlhdHMdiT3DNJZKnn3va1qIpAC1atqZVt76sv2wixaRh3r8GPvkzlRlr0rgUaeSX1elcu2EiJd3Mvyf0bDmmp1NTb+ZsMTFliAv9m9jRpo4zb/V1xS91H5s3FZzUafjTE/nynwxuJFqSOSWmGvn87xSeGJM7RFOlUvHCxE+ZMD+TnSeTqVvVkTn7TRwKV+Hu7o7BaGbenkSqN+2Kt7d3fkuVKhEREayds5CX67ahdZXqhCclMMy/Pu20ntTz9KNvtfr0tfPj+88mFz5ZAWss+20JDwT0oJ5Pbep71+EB/57M/uFXEhISCp/Ahrz76TscN+znUsJ54tPjOBN/nESvaMaMHV3msri6ujJi3FMsi9hOaMJ1wpOjWR+xh+rt6tG0adPCJ6igSJmJzLxS7KMItAEuSilDpZR6YBEwOLcMcreU8uaHdS9QNas9Ukp5OOvvFOAMkMtTUljMg0OAP29OBzgLITSAI6AHyjQ2WrEYWIm6deuycOUfLFq4iNCzl+jWrgcP/e/hSusAVF6QUnL+/Hni4+Np1KgRbm5u+faZMG4k/RpH8PInwWzbZ+CXJWG8PtiBFrWcGD8rmW2njGw/bUQlBFoNGEwCqfHAyzEeLydBRKYaddZdWf9GdizatII+ffvlWctgMHD8+HFUKhUjX5nC29O/JCPlBloHd4Y99Tm97u+bZ0znLt2oWu0f/lkyl4SrkQx/dRzbD+1i4R8nQaWla7/hvP1C2eR82LN7Ny0dPFFlbW2cig7n1Rr3kWrQk5aWhpubO428g1hzaG+x19i9ezfVqZIrGZBapaaaKoADBw7Qu3fvEp+Htahfvz6LVv/Bn/MXEXo+lH7te/G/IY8U2Sm1tHniyeE0adGUpX/8RVyajqceHE+37t0rddSKkCCs6ysQBFzL8fg6FmvAnRgD5CkhKoSoAbQA9t32VGcgWkp5swrd31gUj0jACZggpSzTqlqKYmBF/Pz8GD9hvK3FuGeIi4tj0vjReJmuEeAqmXpN8MATL/H4iKdy9Tt06BBBztd5oJPF9L1hfwqfDHPCxR4u3wCz0DDnDU+ik8Dfy4HwGxks2aZj7cEUIhJMXI/TEBB0y3SZpDPi4u6RR56DBw/w9bvjae6nx2SGk/EOvD15WpHu3mrWrMmrb354q+Fp2xTTcXF1RcetH107tYYMkxEzElWWYmQwm1Bpi/9T4uLigl7kzfBnEAabXWDvBn9/f155rfzUf2jatGmlthDkRZbUidBHCHEwx+OZUsqZOR7np1XJ/CYSQnTHohh0uq3dBVgKvCKlvP3u/3FuWQvAYqEwAYGAJ7BTCLFZShlalJMpDRTFQKHS8Ok7LzO6wRXa1LbUEjCaJK8t/Z6GTVvRrFmz7H5hYaE0rGpk17E4tu6LYu+JePo0dOLM1Qz2nTMj1YID5yXr9qWAyKBt8wAyzYIl/+mQ0pOLaU7UysrDoDeamXNA8sqXI3PJkpaWxldvvcCPD9jhlZXIKTpJz2tvjGX+yh13nQPBVnTu3JlfPp1Mx/Q0vB2d6VwjhBVXz9Pdt0b2RXtD+Dn6P/pQsdfo0qUL32m+ITkzBTd7y2sVn55ArH3yXYXBKty7lNBiECulbF3A89eBajkeVwUi8sggRFMsToP9pJRxOdq1WJSChVLKZbeN0QAPAzljlocB66WUBiBGCLELaA0oioFCVq51lcqqZkCj0YharS6XpsabOeWLUk44MTGR5PBTtOl6q8CQRi0YeZ+K1X/Po1mzb7PbQ0Lq8u6P0TSrms7/Omip56Xhg/kJjO6i4f0H7Zm6TsfUP6J4ta8dLk4qJi44R4+Ggl+erE54koYPF0ex7ISglr8zJ6LUPD72/TwJZHbs2EGvGnq8XG7d8fq729G+ShJ79+6tMLksnJyc+OTnKXz4ymv4m9WYpOSkNpNoQwR1ww1cz0ilSZf2PPXcsyVa45sZ3zHppTdwTXHEjCTTxcSUWVMrXbZQBSsg7BD2wdZc4QAQIoSoCYQDQ7FcvG+JIEQwsAwYIaU8n6NdAL8BZ6SU3+Uzdy/grJTyeo62q0APIcQCLFsJ7YAfSu90CkdRDMohhw4d4ssPviAxOhGpljz0+MM899LzRbpAFnmNgwf54eNPSY+LRy9gwGOP8sy4caW6RnGJjY3lm0/e4vKZg5gl1G3WnonvfIanp+cdx+j1ehy1eZUbJzsVmQm6XG3u7u4Y9TrS0jL5Ykka0Qkm6lURtKoh8HXTEJ0o+XWkmjSD4L9QI6M6CDrXU+FbxZGmdZy5L8SZF/60Y+CEaUysVw8HB4c866anp+OkyRtC5ag2kZGRUYxXxXY0bdqUvzev59y5c6jVakJCQkhKSuLq1asEBQWVihNk06ZNWf3v2lxrlEdlVaEcYs5EpocV3q+YSCmNQogXgQ1YwhVnSylPCSGey3p+OvA+4A38kvW5NWZZIToCI4ATQoijWVO+LaVcm/X3UHJvI4AlAuJ34CSWbYzfpZRlGueuKAbljMuXL/PWc2/Q070D7v5umMwmdi/Yhl6v55XXS8fh7PLly3z+8qu8UKM+vv61MJhNLP1nDdMMBsa9attsemazmVefHcbYpjG0HemClJL/zu/itRdGMOuPVXe8WPj6+pKq9uF6XDxVve2z21ccN9D1yUdy9T148ADSZOTh9va0rqXi2g094bGS7zdkMLBFJq1raHBz0nIj2siOsybeHqjBw1mNLi0VZ2dnfNy0+DpmULNmzXyVAoBOnToxcYaGh1qY0Wosylamwcy2qxqGti3Ib6l8olKpaNCgQfZjDw8PPDw8rLqGgkJRsXYeg6wL+drb2qbn+PtpIE/ImZTyP/L3Ubj5/Kh82lKxhCzaDNvfHirkYv5v82imrY+7g8WbXq1S08GvNWv+XoXBUDolWP+c/TuDvQPxdbKY3bUqNY/WrM/GpctLbY3isnfvXhq6RNM2y09ACEHneq5UU4dz5MiRO44TQjDpkx95c42KhXuS2HoykXdXpJIR0J3u3Xvk6hsVFU2LGoKODRyxt7dHq9XQob6WmgEacPAlLl2LSqPB3sGJ6lW8iE1TYzAJNBqLWdtsliRniDsqBWBxPH3gyQmM+yeZtcfjWXUsnheWpTDshXdwd3cvhVdKQUEBsBRRUjIfliqKxaCccSX0CiFOgbnaVEKFg3AgNTW1QHN6UQm/fIW2rrkvTmqVCje1hrS0tFK/E7wbwsOvU8tDn6e9pruBqKioAsfWr1+f3/7ayob1a7kaG83jQ7rQtGnTPFYGXx9vhI+KDL0ZBzsVnq5aopIyqeGrwsXZkRSDPeuOZdChSRCD2zny7dIkXumjpWGgO1JK/tyVQsuODxXqQPjIY0+QkJzCtIWzECoVjz35HAMGFd9JT0FB4Q5I61oM7jUUxaCc0aZjG47M3UMzh0bZbRnGDEz25lK702zWri3HVmygZ/CtlLNpej06jSrfuP+ypGnTZsxc4kDOy6eUkr3XNUxq1OiO427i4uLCI/8bUmCf5i1a8suKqkSnJIFZDwgyTfb8c9BEYJCGWFmVWae1rLxiwslOEKOux4f/Qs2TRm6kQOM2A5n4+vuFyvLeGy/in7qdOaOdMEvJwl0/8HnYOd795JtCxyooKBQRlR0qhxolmOD2tAIKimJQzhg6/HFWLV2JOvYsdTxqkJCexL6Uo7z6+Rul5hj42PAnGLN8BdrrYbTyCyIqLYUl4aE89+G7Nnc+rFevHg7B7flp6w6GtHLCLGHhfh1BTftRvXr1Ulmjfv36uFbrzMrju3ikgyNmCSt3ptOu3/08/fxEvLy8sLe3JykpiczMTHx9fZFSEh0djbu7e5HqApw5c4bM8N2Me/SWMjehn5ZX/tzMlStXSu1cFBTuecyZSF2ZRfLdEwgp883TUOFp3bq1PHjwYK62s2fPsmXTv7i5uzJgYH+8vLxsJF3BJCQkMG/2XPZs241/YABPvTAmVxx+aa2xcPbvHNz5H76BVRj+7NhSX6O4mEwmVq5YxuaVf6JSqek9+AkGPDCoWErLmTNn2LVzC84ubvTpOzD7PTeZTMz6dSbL/pyJSqh47MlxjHxyVIkVo8zMTDZv2siaFX8TkLqVSQ8HoVLd2spYuDMR397f0r9//xKtU1FITk5mw7r13IiOoX3njjRv3lyJNrAyMTExrF+7nnSdjp69e1GnTh1biwSAEOJQIfkCikWrBs5y7+z6xR5v1+GwVeSqyNwTioGUki8+/ZJ1f/+LmzEQkzCQYhfOV1M/o2PHDjaWVMEaSCn5/uuPuXJ8Jf1aCZJSJcsPqHjlnam0bduOxQvnsn7B9zzYwIzJLFl+Vs0jY99h8MPFdwaOiori1WeG0sk/iSAHHTtOR5Iq7Pj26do42FkUjk9XpjP4ld9p0aJFaZ1queX06dO8/sxLNBB+uKkdOaePpmqb+nzx/dc2t0xVVrZu2coXb35CiLo6WtRcNF2j3xMDebEcZGC1mmJQ30nu/a1e4R3vgF2no4picBv3xFbCiRMnWPfXNhq4dsm+W8k01uSd1z5ky3/rCqyKp1AxOX78OBGnVvDtsz7Z73nP1gZe/Pw1vp/+F6vn/8D0/7lmhxL2bmzmmelf0K1n72L7cvzw+bu81DyVljU8AHfquafy7/lUFu2I4cme/uw8k8Z1Yw2aN29eOidZjpFS8sGrb/GIZyu8HC1+K02pxcp9+/l361Z69uplYwkrH5mZmUx+5zMe8O2Bg8YSsttAhrBq4Wr6DRpA7dq1bSyhlVDZIxxqlmCCo6UlSaXhnlDb163eiKe0ZMZKT08nMzMTe40j6kwnzp49a2PpFEqKTqfj2LFjhIeHZ7dt27yaQW01uczWnq5a6lXR89dff9G7piFbKQCw06joXsPEvn3Fd0QKPX2EljVuFskSVAuuRecQT6ZtiOeJXzP5N6UL3/w8/46m9PzOozgkJCRw9OhRbty4UaJ5iotOp2Pbtm1k3EjOVgpu0sK9BhtXrL3DSIWScOzYMfyFV7ZSAJaIptrqamzesMmGklkZcyZSd6nYh0Je7gmLgbOLEylpyaREXkAl1UgkKq2KTN/0AmPRFco/i/+cx/LFv9CsjpbrMUa0bvX4ZPJPODg6kZaRd5tMlwk1XF3RmfLqxDpDwbkJCkMKFWazzPYp0Gg0uPsGcV+H5vwyb1mB5vM/581l6ayZ1HdxJCpDj3PNOnw25UdcXFzuOOZ2zGYzU76azP51q6nr6khoagZ12nXknU8/R6Mpm6/6vN/nMX/aXLxwJyw0gpkJaxjRrBeOWRcrvcmAo7OSx8EaODg4YJB54/KNmHAsgsNshUVKa1dXvOe4JywGbdvfx+novdjhgIPKBUeVKxkZaVyNuUCtWrUKn0ChXHLw4EH+WzeV398J5LUn/PlhQhC9G13mq8/fpt/AR1i000R65q0fjLNX0ojN9GXIkCFsvWJPXOqtZE7RSXr2RDrSrl27YsvTrnt/VhxLzH4spWTO/hQGDhldoFJw8OBBtv42k69a1mNs/Rq837wu9yVHM/m9d+9q/X+W/k38tvV83aYuYxsGM7lNXZxPHeD3GdOKe0p3xYEDB1gy9Q8e8L6fLr7t6V+lFx6ZPiw9tRMAo9nEruQLPDzMpkndKi2NGzdG55xJrO5Whd4MYyYXzFfpNyBvSfBKhdlc/EMhD/eExeDgvkPU9avP+biduAhfjGRiUqdTw78Wly9fpmbNkuxPKdiKlUvn8PQgTzQ5tgR6tfVm/sZ9+Pn58fgzHzPmh4+5rw4kpcHVJE8++3YGzs7OvPH5NF559yWa+aZiMsOpeEfe/Xpaiaoejnv1Ld577Rr/rTxGbS84FgXNuz3KgAcGFThu2fy5PFYjAE0O5aFz1QBWHNxv2fayty9g9C1WLpzPpHrVcm1VPBxSjUn/LOOZcS8V76Tugj/n/EFzl8aos8oxV61WFZPJxKro9awNP8Q1UyIjXhxzj5UELjtUKhU//DqVV54Zj1OsHRqpJoZ43v7yPXx9fW0tnhWRygW+lLknFIPkpGSCPWrTploXbqRGolXb4+3kx8m0vaSlpdlaPKvx346d/Dj5O1Lik3BwdeKZl5+n/8ABthar1NClpeDmnPsjLITA2UGFwWCgb/+BdO3ekxMnTuDi4kKDBg2yL5qtWrVm4aqdHD9+HCEEbzdtWmInVAcHB77+aTZXr14lIiKCkSEhRSowpEtJwfU2hUQIgYNajV6vL7JikJmejpM2dwiunVqFyVg2aa5TklLw1gZkP1ZrNNSqU5uqDtV5/LPxtG7dGldX1wJmUCgptWrVYvmmlZw6dYqMjAyaNWtWYUp8FxuVPTiVxLHyXKmJUlm4JxSD+/v1YtNf7xMkqlPFzeKEmGnMIF2TTL16xQ9zKc8cOnSIL1/9kEH+7XALdEZnyGD2Rz8ihKrSmBU7dhvIml3f89wjt/ZPr0bqkFrf7AuQo6Mjbdq0yXe8RqOhZcuWpS5XcHAwwcFFLwPbpd8Atvw+jeH1b21rhSenovHyvqsLabsePdl+8F961gzKbjscEUvDVvcVeY6S0PuB3qz6ZhltHW6Vlo9PT8CvegDdu3cvExkULJaDJk2a2FqMssOUCakXbC1FpeKeUAxatGhB+36t2LNuB34EY5CZRKvD+Ojb9yptvfeZP/zC/d4tcLN3BsBJ60A///uY/dOMSqMYDHzgQSZuXsXX8y/Sqak912MMrNht5sPJs20t2l3xwIMPsmXNKmacvkRrDxci0zPZkpTO5zNm3dU8Tz0/jnEjdhF5+goNPZy4mJLO3gzB1O/fspLkuXnw4YdYv3I9O8/vJUhbhRRjCpfV4Uyd9nOZrK9wryKVYkilzD2R4AgsjmDHjh1j0/otuLm7MvihQQQEBBQwQ8XmgW59eMKzS57QuLlRW1m7e7ONpCoddu3axerlC8jISKNTt4F4evpw4uge/AKq0X/A4ApZvdBsNrNz504O796Ff9WqDBhUvPPQ6/Vs3riRcyeOUbNuffr074+jo6MVJM6fm+exb+deAoODeGDwAxXm/di1axd/zf2TdJ2OPoMHMOihwWUWzXEvYK0ER61D7OW+H4MK73gHNP3DlARHt3HPKAb3Gq+/OAGf0wZqeNyq1BiXnsQ+5+vM/XuhDSUrGXNmT+fE/oWMfiQQZyctq7ZEERZbnW9/+FXJpqdQbGb8NI2tc1fQ2asB9mo7DidcQtXAm59+m66kcC4lrKoY/FCl2OM1A68oisFtKOpwJeX5V19i3LCn6SzN1PQIJCIlli1Jx/n0069tLVqxSU5OZuPa+fz2eaPsXAHPDqvN5OkX2L9/f4lCDRXuXZKSklg5/y9GVeuOSliUy16OLVhxZh8HDhy4o4+KQjlBZQ/OJakHcaXURKksKIpBJaVWrVpMWzSbGVN+YfGJPdSsU5tvxv9Ew4YNbS1asTl79iwtGzrnKkoE0KGFM0cP7y0zxSA9PZ2tW7aQEBfLfe3aV1oH1uIgpeTUqVMcOnAI/yr+9OjRo9x7xZ85c4ZqWm/SjXpOxFzCYDLS0KcGtbW+HNi9T1EMyjvmDEg5b2spKhWKYlCJqVGjBl98/5WtxSg1/Pz8uB6lz9N+NVKPX83i7zHeDZcuXeKt50bR2dOMnwNM/XM6Aa278c4nX9zzJmez2czE8a9zevd5PIx+ZKp1fOc6hVkLZtxVlEZZ4+fnx8XEcA6cPU1Nuxpo0LAnbAPu7o48X22grcVTKAyJkseglFE2ZRUqDDVq1MCkCWbzrghu+sZcupLEpj2Z9OlbNmWMP33jFd5t5sKIZlXoU68Kn3QKIv3Yv+zYsaNM1i/PrFmzhvM7r9DavSt1vBvQyKMVNTOa8PbEu8vgWNZUrVqVKzei6eTcgcYu9anvUocebl24EhdHvQaKNahCIGXxD4U82MRiIISYADyNRdc7AYwGnIDFQA3gMjBESpmQ1f8tYAxgAsZLKTeUvdR3T3p6OhvWrufypVCatmpO127dbFrJMTo6mnUrV5Gu09H1/l423VaQUnLy5El2bduIi5snffo/UKTsbJ9/+TNfffEeC1YewE4rcHIN4vOvZ+Ps7FwieQwGA9v+/Zezx49Tq149evXunSex0I0bN9CmxhLsecs6IYTggVrurFu1lK5du5ZIhtuJiYkhMTGRmjVr2jysNjQ0lE1rNyBUgr4D++drAVj51xpqOuW+kPo4+3E+9AhpaWklfo+sxYkTJ2ge3Bw7nROxacmAQGOnoWONDvy3/b9Cvyc6nY6N6zZwOTSM5q1b0LlLF6Via1misgeXkBJMEFFqolQWylwxEEIEAeOBhlLKdCHEEmAo0BDYIqWcLISYBEwC3hRCNMx6vhEQCGwWQtSVMp9qIeWIyMhInhs2mtoGN/zt3PhrxX/87v8rMxbMxskGBU3+3bqVH9/5gG5uvjiq1Hy55B9aDOrHK2++UeaySCn55vMPiDy8gn71TCSGwvg/pvLS+1Pp0LFTgWNdXV355PMf0Ov1GI3GUnktU1JSeGHECGqlp9HA2YVjmzcx7+ef+Gn+glzKikajwZDPDYbBZEbjVHr76Kmpqbw/8RUSLpzFx0FLqM7IM6+/Sd8BtjFrz/vtd5bNWEBLh2qYMfPinMWMem0cDw/5X65+dnYaTGZjnvESc7mOGNFoNKCCqsHVMGfdRapUKk7eOI3WrmCFLCIigmeHPUWw3hMfjTvzlu1idtCvzJj/W5mGid7TmDMgWcleWJrY6tuqARyFEBosloIIYDAwN+v5ucCDWX8PBhZJKTOllGHARaDcewN99eFndNfUpEdgExr5VGdAUEuCYgTzZ88tfHApo9fr+eGDj3k9pDldq9akTWAwr9RrwZGV62xSdvro0aNEH1nO5Add6NbQgwdbeTD1EXumfPo6RmPeC0t+2NnZlZqCNXvaNDqZjYwICaF1YBWG1KnNwy7O/Dh5cq5+np6eOFWpwYnIpOw2s1my+EIyAx4dViqyAHwy6Q3aJIfzWes6TGhcna9aVOePrz7j/Pmyd7CKiori75nzGV29Cy0DatM6IITRwV347dufSExMzNX30eH/41LGKaS8td8bnnKF+s3qleuLZJMmTUh10JGYkYhKCFQqFXqTgTB5pdBkYJ+/+zEdVPXo6N+Met416BVwHx7hggVz55eR9ArZPgZKEaVSo8wVAyllOPANcBWIBJKklBsBfyllZFafSMAva0gQcC3HFNez2so1Z4+epI5nYK62+/xD2Lx6fZnLcvLkSULsnHDS3rqrFULQwc2HfzdsLHN5dmxezQMNyeWs5+6koYGP3iYXv92bN9MlKPdHqqW/P6fyyYPxwddTmBVux9f7I5l7LJIX/71Gy4dHlVpq5eTkZK6dPEaXav7ZbY5aDf+r6sk/CxeUyhp3w67//qOhxh+1uPVToVVpqKf1Y//+/bn6du3alT7DerAneROnEw9zJHkXaYExfP7Np2Ut9l2hUqn4YeYP7DUdZHfcPvbHHWJ9wiYmfvRagUnQpJScP3mW6u65Y+ib+9Rl86qy/57f05hl8Q+FPNhiK8ETixWgJpAI/CWEGF7QkHza8n03hRBjgbGAzb2ghVqFSZpz/aBmGPU4OZX9nZODgwPp+aQM1ZmMeNugqI2TixupsXnfwpQMiYODQ5nLY+fgQIbJhDbHvrDBbEaVzz6xn58fc5at5vjx48THxzOiWTO8vLzy9CsuOp0ON7u8X0tPR3uSE+LztKekpPDTN9+yf9t2QNDx/l688OorpWZNcXJ2JpO8VpxMjHmsAEIIJr75KiNGD+fkyZP4+fnRqFGjChGtUa9ePVZtXc3hw4fJyMigdevWhb6GQggQArM0Z+c/AMv33NGl7LcL710kSOXOvzSxxVZCLyBMSnlDSmkAlgEdgGghRBWArP9jsvpfB6rlGF+VO3iLSClnSilbSylb27rMaPcBvdkTdWvfS0rJ5qiTPDrqiTKXpUGDBsQ6aLiSlJDdlmbQsyMllr42qLbY74FHWHRcjS7zlrJyOlxHgirAJiWwBw0bxt+hYeTMArr68hV6Dh6cb38hBM2aNaN79+6lqhQA+Pv7k6iyIz49I1f79sgEOtzfJ1eb2WzmpdFjcDtwkvfqNOfd2k1R7TzAxGefp7Qymnbt2pWz5liSMm9VIY1LT+aKSKZt27b5jvHz86NHjx40bty4QigFN9FoNLRp04YuXboUWbHq3q8Xh27c2o4zSzO74k7w2OjS21pSKASVA7jWK/6hkAdbRCVcBdoJIZyAdKAncBBIA54EJmf9vyKr/0rgDyHEd1icD0OA/bdPWt4Y/9oE3g5/nTlHduKndeVaZgLdH+zLgAfK3oFMCMHXM6bxxnMv4BUXjqNKzSW9jlc+/xg/P7/CJyhlqlatyshXvmDsd+/TvEoGSRkQbQ7gsx9m2eRC8siQIVy+cIH3N20kxNmZy7p0qrVsyYQXXihzWYQQTPz4Mz5+/RUG+bng5+TA7htJxPrXoE+/3PvdBw8exDM+mY61Gt4cTI9qNTl/8QRnzpwplagTJycnPvvpGz549S0CzE6YpSRWm8nXM6eW+8RFZcGESROZFPk6fx3bhrfalXBDPH0eHUDffpWjUFmFwJQBSWXvK1WZsUmtBCHER8BjgBE4giV00QVYAgRjUR4elVLGZ/V/B3gqq/8rUsp1ha1RXmolREREEBERQa1atUr97vJukVJy9uxZ0tPTady4sc1/2DMzMzl16hQuLi6EhISUqVIgpWTH9u1sXrkYlVpN+54DOXviKMcP7KZu4+Y89dyLNlGabhIVFcWKv5YQHxNNm67d6da9e54QuCWLF3P914X0qJE7Hez6Kxdp9urz9CvFi5PJZOLUqVMIIWjUqFGuKIPDhw/zx9zF6HQ6Bj7Un759+5TrKARrEB4eTmRkJLVr18bT09PW4pRLrFYroZZG7vvErdjjNcMTlFoJt6EUUVK4J/nqk/dIPraKR5s4YtAb+H79JZwdXXm1Zx3ORutYdEnw+YyF1KpVy9ai3pGjR4/y6ytv8Eydxrnap54/xqTfphMSUpLY7qLx++y5/P79HwRp6qNR2xGZeYn6HasxddoPFWobQcH6WEsxaFVTI/d9XHzFQDuycMVACNEXmAKogVlSysm3Pf8E8GbWw1TgeSnlsYLGCiE+BJ4BbmSNe1tKuTbruabADMANMAP3SSlz7y9akXtLrVdQAMLCwgjbt4Z3enlTL8AZH3Uq3/ZxwpCZilol6FXfm9daafnpy49sLWqBNGvWDHNwFdZfvYjeZCTdaGDl5fN4NKpXJkpBSkoKv/00lybuXfFxCcTD0YcGHm05szeMw4cPW319BYVsrBiVIIRQAz8D/bDk23k8K79OTsKArlLKpsAnwMwijv1eStk867ipFGiABcBzUspGQDfAUOzXphgotRLy4WZWvvPnzxMcHEyrVq2sbhrV6XT8999/pKen0759e5uasW2B2WzmwIEDhIeHU79+fatmZTx08CBdgozZd7TpujR83TR0CFZzPDyZKu4O1A9wJeJQ+S7MIoTgh19nMnfWLL5duRohBH0ee4i3Ro0q0niDwcCuXbuIj4+ndevWdx3Jc/r0aVykDyqRe4vDzRTArh27adWq1V3Np6BQLNQO4F6/BBPsLaxDG+CilDIUQAixCEtk3embHaSUu2+bsGpRx+ZDb+D4TYuDlDKuyKdSSiiKwW3o9XrGPfMi4SejcTV4oNOm4FTNjlnzf8XVSqF9R48eZdLzr1JD+qBFzXTTFJ54cTTDnxxhlfXKG4mJibw0ahRVUlMJ1mrZlJGBU/36fDl1qiUrXSnj4+vLYd2tedVqDUaTiagUSUiwxe8iLdOIxqH8JuW5ib29PWPHjWPsuHF3Ne7KlSs8/+RzeKS54mi251cxg66Du/Hmu5OKvAXg7e2NQejytOuFjoCgO8f/KyiUKsYMSCiR86GPECLnvvNMKeXMHI/zy6WTf0iOhTHATT+4wsa+KIQYicUBf2JWGYC6gBRCbAB8sST4K9NqeMpWwm3MmT2XxGM62nh0poFvE1p5dMDuqivfTP7WKuuZTCbeGf8GD3m1p2tgCzoENuWJoB4s+mkuly9ftsqa5Y3vPv2U+4Xg6Xr16F2rFhMaNsTz0iWW/PGHVdbr2LEjB+JdOR9lCcHz9PFlx+V0TsUKWlZzx2SWTN8fy6DHn7LK+uWBN8e/QWvZhA6+99HCvyn9fXuyd/lu9uzZU+Q56tSpg29ND6JSbtWzT8lIJNUxin79+lpDbAWF/DGX4IDYm2HuWcfM22a/m1w63bEoBjf9DQoaOw2oDTTHkuzv5kVGA3QCnsj6/yEhRM/8T9w6KIrBbaz8exV1PXKbsWt5hLBzy39WWe/kyZP4mJxxs3fJblOr1DS2q8aGtUXLnqbT6bh27Rp6fd6SxBWB43v20LZK7uxx/atXZ8OyZVZZT6vV8uW0+Uy7GMiLKxKZ9K+Z367XQedanXe2xTN2bTxVe43m0cdLP+eEXq/n6tWrpKenl/rcRSU2NpaUyGT8nG/l+hBC0NAphBWLl9/VXNNmTcWjhZljaZs5ofuXBJ/zzJz3s9WsawoK+SJLcBROkXLpZDkMzgIG5zD/33GslDJaSmmSlhziv3Ir1f91YLuUMlZKqQPWAqWTWrWIKFsJt6EqY09qIUS+n02JLNSkazab+frzr9iychNuGheSzamMGvcUw0aUfRKlEpHPeZqzCtlYi+DgYKbNX0piYiJCCNzd3TEajcTHx+Ph4WGVUM75v/3GP3PnEGhvT2RGBt0ffIgXJkwo89A+S8a+vO1Sgkp9d7J4enry6+/TSUlJwWAw2DwkV+EeRO0AHlb1MTgAhAghagLhWIr65cpgJYQIxpKsb4SU8nxRxgohqtwsAwA8BJzM+nsD8EZWrh890BX4vnjnVjwUxeA2Bj86mLXTNtPE65aCdjHpHN0HdLPKeo0bNyZBm05CRjKeDpaQG6PZyEn9NcYN+LjAsTN/mcGplYd5xK83QgiMZiOLf1hA1eCqdCnlEsDWpEWnTuw+e5aOOeoVrLpyhb5PWd+U7+Hhkf23RqOxmtPnhnXr2Dd/Lp83aYRGpcIsJXPWreVPb2+eKKKzYGnh7e2NZ1UvImOiqeJqqclglmZOpZ/j3aEfFGtOxUKgYDOMGRBvvQRHUkqjEOJFLBdsNTBbSnlKCPFc1vPTgfcBb+CXrBs6Y9a2RL5js6b+SgjRHIvd4jLwbNZ8CVkJ/Q5kPbdWSrnGaieYD0oeg9swGAxMGPcqFw+F4Wr0QKdNxbOmKzPmTLdaPfmTJ0/yxnOvEGTyxE6quWSOZsyrz/Po0CEFjuvbqTeD3LuhUd3S7+LTEznvG8HsP+dYRVZrkJyczCtPP41bXBzBWi2nMzLwa96cT779ttLUtX/qkUd41tMN7xz1BdINBj4NvcLijWVfyCo8PJznn3wOp2QHHM12RBBDv8f688rrE8pcFoV7A6vlMaiukXvfLn4eA7vnlARHt1NpLQahFy6xZtUaBjxwd7UAtFotP82cyvnz57lw4QLBwcFWz/neuHFjlm9dy549e0hPT6dt27ZFyp5mNhhzKQUAbvYuxMfmjW4xmUwsnPs7G/5ejMFgoHXnrowdPyHXHbOtcHNz47fFizl27Bjh4eE82KBBuU4sVBxSk5PwrJLbGuGo1WLMLLOcJbkICgrinw3L2b9/PwkJCbRo0YIqt/l5KChUGJQaSqVKpVUMXHBk9ke/kBifwBNPFlS8MX/q1q1L3bp1rSBZ/tjZ2dH1Ls3/VWsGEx19A3+XW05k5+Iu0Wlwlzx9v3j/HexP/ccXzQJx0KjZfuE/XnzyAL//vQKtVlti+UuKEILmzZvTvHlzW4tiFZq3b8/+E8dpV/XWdsnZ2DhqNrBevobCUKvVtG/f3mbrKyiUChIwK1k2S5NKqxiohIo+AR2YN2MOQ4c/XmlM0jl565N3eHHk89TPqIGPgxfX0yOJcknkw+e+ydUvJiaGC3t38G3HGtlt3Wv6E3biOlu3bKZPX6Xgi7V55qXxvDDscRLCLtPQ05OLSUlsSknl28llGp6soFD50JTU+XB34V3uMSqtYgCgUWlwlHYkJydXysImISEhLFj5J0v+WEzouUt0bteHhx55KI8vxOXLl6nvltcq0MDdjktnToOiGFgdX19fflu6jBVLl7L1yBFqtu/IzKFDFS/+CojZbGbr1q2sWbUBTy8Phj3xGHXq1Cl8oIJ1MGYg488V3k+hyFRqxcBoNpIu9Li5Fd8xpbzj6+vLuJdfLLBPjRo1OJecN8fB2SQ9TW1oyr7XcHNzY8To0TB6tK1FUSgmZrOZ554Zx8kDV3HVBKI3RrF62Rg++OxNBgzsb2vx7lGEspVQylTaBEdmaWZj1B6GPzOyUm4j3A1+fn7UatOZOSeukW4wYpaS7ZejOWp0pnuPMk2opaBQodm1axcnD1ymqnsz3J198XWvRlXnNnz+8dcVNsFYhUcCUhT/UMhDpbUYpJDOk++N5YHBg2wtSrngnU+/YP7vv/Hm0iUYjQZadujMT1MmWiWRj4JCZWXLpm04qfxztWnUWlSZTly8eNGqxb8U7oDGATxL4mNwo/Au9xiVVjGoHVJbUQpyoFarGfX0WEY9PdbWoigoVFj8/HwwmE/maTeRWS5Cf+9JjBnIOMXHoDSptFsJCgoKCqXNI48+TIYmCr3hVq2LuJTr1KwbRGBgoA0lu8cxi+IfCnlQFAOFEmMymYiNjcVgMBTaNyMjg/j4eCprxk2Fyo2/vz9TfvmSJO1pInSHuabbS7Umdvw8bYqtRbt3uZnHQFEMSo1Ku5WgUDYs+XMR837+FVc0pJj1DB7+GE8//2yeTJF6vZ4vP/yEA//uwFVjR6aDhokfvkf7jh1sJLmCQvFo164dW7av4/r167i4uCghpzZHIBUnwlJFUQwUis3WLVtZ/sNvPF+9HXZqDSazmWULVuHm7s5jTzyeq+8X73+E6sA53qjTASEEyZnpfDnxLX5YNJcaNWrY5gQUFIqJSqUiODjY1mIoAGgcEN4lcT6MLLzLPYaiGCgUm/nTfmVwlabYqS0fI7VKxcCgpsydMz+XYqDT6TiyYxev1W6fbUlws3ekt3swS+Yv5I333rGJ/AoKCpUAYwbyxvnC+ykUGcXHQKHYJMTF4+GQO8uio8YOvS53YaDk5GTcNfZ5thd8nFyICY+wupyVjYMHD/L0E0/Rv2tfXnvpVcLCwmwtkoKCzZASpFkU+1DIi6IYKBSbFu3u43TstVxtV5NjqVanZq42Pz8/ktVm0vSZudqPJ0bRrsfdFY6619m+bRvvjZ1EnWg/HnLrjtNxE88OfZqrV6/aWjQFBduhJDgqVRTFQKHYPPvyi2wzXGdf1EXi0lM4Eh3G8oTTvPreW7n6qVQqXnp3EjMuH+J0bATRaclsuHaWKx5qBg5Sck3cDVO/nEIfv474OHkhhCDYPYj77Boy/cdpthZNQcFGFN9aoFgM8kfxMbhHMJlMzPl1Fuv+XobZaKRFxw68+NqrJSouFRAQwLQ/5/LRe++z8dB/1Kgfwvef/ppvQZkePXtStVo1Fs+Zz4moKJrc3xvz+QsM69Mfrb09A4Y+yvBRo1CpbKOrHj16lOlff8ON8Ag8fHwY88p4OnTqZBNZwFIR85+/lhEVEUWXnl3o2q0bKpWK5LhkXAJyb98Euwey7tgeG0mqoGBbhMYelU+9EsygWNtuR1TWePLWrVvLgwcP2lqMcsP7r7+BPHyGfkF10KrVHIq+xr+kMX/5smKnRU5MTOSZoSOoqbOnppMP4ekJnBAJTPtjToHJXtLT0xk5+GH6at1p4R9IpsnIyqsXcOvchrc+/qi4p1hsTp48yUdjn2NM9boEuboTk5bK7LCzPP/FZ3Tq2qXM5Tl44ABvj5tEfVUN3OxcCM24hmsDb37+bRqP9HmQ++3b4qCxz+5/PTmS5Ibw3S/fl7msCgpFRQhxSErZurTnbRloL/97tvjJpZw/vGwVuSoyylbCPUB0dDTn9+znwer1sddoUAnBfQHBhGTC1s2biz3vrz9No0WmB72CGlPbM4AugQ24374G33/2ZYHj1q9dS1OzhlYBQaiEwFGjZUjNBhzeso34+Phiy1NcfvthCiOr1iHI1R0AP2cXnqndkFk//FDmskgp+ejND+nj0ZHGvvUIdg+im387dGcS2bhhI0+PH8um6N2k6XUAxOkS2J16jLHjny1zWRUUygvKVkLpoigGZYher2f37t3s3bu3TCuxXblyhRoOrnnaa9i7cvHs2WLPu+ffHbTwy+1oGOJZhTNHTxQ47sLJ09Ryzl0KWwhBsIML4eHhxZanuFwLDaOGR+4tFW9HJ1LiE4o138WLF9m2bRuRkXcfHx0dHY1aJ3C1d8nVXte5BpvXbOSBwYN47qOX2CGP8lfMRk66X+XLmd9Qv35J4rgtpKSksGPHDg4fPozZbC7xfAoKZYWUotiHQl4UH4MyYu+evbw94V3cDJYsaSl2CXw59Qtat7a+BatGjRqEZaTmaQ/LTKVLgwbFntfJxZk0QyZu9o7ZbQazCbW24I9V3SaNOLZzP418ArLbpJRcyUihWrVqxZanuATXrk1YYjw1PW5lsIvVpeF6lxntdDodrz8/jszL16iqdWBGRipNundh0kcfFtl3wsnJiUxzZp72NIMOD09fAPoN7E+/gf3vSrbCWPrXUn768me88cMoDBjd9Pzy+89KEh+F8o/GoYQ+BqGlJkplQbEYlAFpaWm8+dLbtNJ2oal7G5q6t6GFphOvj3uTjIyMwicoIX5+fjTq3J6/w06RbtBjMpvZE3GZMEdBj169ij3vo6NHsD7yBOYsPxUpJZsjTjFwyMMFjuvbvz+nNCb2RV7DLCVpBj1/hp6mbb8+NqlQ9/SEl5l7/RJXkxIBiExNZualM4ydOOGu5vnhi8nUi0nixZCmPFijLm/Ua0Hcjr2sXL68yHO4ublRt0V9zsXf+rHSmwwcSz/PYyMfL2Bk8QkLC+Pnz6fR1b03zT1b09qjPSEZjRg/9mWlpoVC+ceQiTnmQrEPhbwoikEZsGPHDrxNAThqnbLbnLTOeOh92b17d5nI8O5nn9Jo5BCm3rjA5CvH0HVoxrSF89FqtcWec9DgQbQY0oefLm9nacQRfrm8A59uTXnq2WcKHOfg4MD0PxYQ26o+H108zNSYS7R8egQT336rwHHWomHDhnz86wzWOwo+vHCcZWQw4Ydv7joqYe/Wf+kUWD37sRCCgdVqs2Lhn3c1z6fffE5aiJlVMVv5N3YvKxO2Mu798dSrV5K7ojuzYulKqqtqo1HdsvR4O/lgiDNVyuRJer0+eytPSolOp6t0WycGg4HMzLyWp8qK4mNQuihbCWWAwWBAmPLqYAJVkSoSlgYqlYrho0cxfPSoUptTCMG4CS/z5DNjuHbtGlWqVCnyHX9sbCznT59HGAQpiWmcPXmajIwMnJycCh9sBRo1asTUObNLNomU3P4zY69W3/V77OrqyvQ5M4mOjiYhIYFatWoVO3KkKGRmZuRSCm6iFncve3kmKiqKN157mzOnL4CUeHq7kpqqw2QAjVbw3AtPM2LkcFuLWSJSUlJ47+0P2bPrAEhBjdpV+erbz6levXrhgysoEhRfgVJGsRiUAZ06dSJWE4HJbMxuM5oMxKmiaN++vQ0lKx1cXFxo0KBBkZWCxMREJj71HL0Nvoyv2ZlXa3ZBu/8y70x43bqCWpkGLVtwMjYqV9vW65e5f9ADxZrP39+f+vXrW1UpAOg/qD9XjWG5tg3S9GlkOqQTEhJi1bXLCqPRyIhho7l8Op0AlxZoTf4c3HOOuMsO+Dk3xV3TiClf/8ayZf/YWtQS8dwzL3J0RyTVnNoT7NKepDAnRg57Gp1OZ2vRrIcUYFYV/1DIg2IxKAO8vLwY/9Y4fvz8ZwLMwUggWn2ViR9MwM3NrdDxlY1Vy1fQWutLFRdLJIAQgvYBtZl+cjdRUVEEBAQUMkP55LUP3mfciCc5G5ZEkNaBMxmpGIOr8MbIEbYWrUCaNGlC78d6sn7JRgJkEAYMRGsj+GbaVzZLOFXa7N69m5R4M75uFgfOK5Fn8HIIwWQU6HQ6nJyc8XGty/RfZvHwww/ZWNriERoaStj5SILcWma3uTp5k5p0g/XrN1TY8yoMobVH5VcSBbb4kVmVFUUxKCMeefQROnXpxOZNmxFC0Ov+Xvj5+dlaLJsQefUavnYuedp9NM7cuHHD5oqBlJLVq1azeM4i0tN19B3cj+FPjsDR0bHAcb6+vixctYId27dz7fIVRjZrSuvWrfMUjyqPTJw0kYeGPMSObTtwc3fj/t734+qaN8T1bvnvv/+YPW0ON6Jv0LVXZ8Y8O6ZE2TaLS2RkJEb9rZ87gyEDV60DRrMeg8FiydNq7IlLSStz2UqL6Oho1NIhT7taOnLt6nUbSFQ2SEMmpqiLthajUqEoBmWIv78/Twx/wtZi2JxWHdqxYtMB6nsHZbeZzGau6ZOoXbu2DSWz8M0X37B/6X/c59kcO7Ud+2fvZPvm7cxbMr/QO2itVkvPEkR62JJatWpRq1atUptvyZ9L+HXy7zR2bkmQfR2OLT7H8I0jWbzyT1xc8iqG1qR58+ao7KZnP3Z19iJdl4hWbZ+t8KXqEqhVr0aZylWa1K9fH71IQkqZSxnVqxJo36GtDSWzPtb2MRBC9AWmAGpglpRy8m3P1wd+B1oC70gpv8nx3ATgaSzuECeA0VLKDCHEJ8BgwAzEAKOklBFCiDbAzJvDgQ+llGW6x1U57IQKFYpu3buTFuTKhusnSchI41pyHHPC9jJk7GibOR/eJD4+ni3/bKS7f0dc7V2w19jR0rcppquZ7Ny506ayVSQMBgPTp8yknVdXPBw90ag01PGqh1uiN0v/Wlrm8tSrV48OXVoQlXiG9MxUAnxqkqg/h8ouA4SZ+OQI0uRl3nvfNpExpYGnpyfDRvyPq0mHSE1PIF2fSnjiKeo3q8p9991na/Gsh8SqPgZCCDXwM9APaAg8LoRoeFu3eGA88M1tY4Oy2ltLKRtjUSyGZj39tZSyqZSyObAaeD+r/WRW/+ZAX2CGEKJMb+IVi0ERiIqKYs2KVaSlptKzb28aNWpka5EqNGq1ml/m/sbSJX+xfs0GXH3deOndT2nXrp2tRePChQv4qr3zmP89Ml2Y+vUUIq6FM2DQwHvSN+QmBoOBzZu3cPzwCeo1DKFvv744OOQ2Yd+4cQNHs1OeaAc/hyoc2nOYJ0c/WZYiA/DdD9+watVqlixahlrtyrg3vyP00mWOHDnOfQ0aMfbZ7yp8QqeXJ7xEsxZNmD/3T3RpyYx8ZAQPPfxQhdjOKj5WDztsA1yUUoYCCCEWYbnTP32zg5QyBogRQgzIZ7wGcBRCGAAnICJrTHKOPs5YVByklDk9RR1utpclimJQCJs3buaHdz+jhX017FVaPl6yntaDuvP6O5NsLVqFxt7enmEjhjNsRPkKDwsMDCTRfOv7KqXk6pUrhMWG4m924NiM1SyY9hvf/z6NunXr2lBS25CcnMzwx0aREaHBWXrxL4eY9uOvzF/8ey6fGU9PT9LMqZilGZW4dVeWkBFHy7q2UaxVKhWDBw9i8ODKXeq7W7dudOvWzdZilB1ae1QBeSu6Fp0TPkKInBX3ZkopZ+Z4HARcy/H4OlCkvRkpZbgQ4hssJRzTgY1Syo03nxdCfAaMBJKA7jna2wKzgerACCmlkTJEUQwKQK/X892Hn/NkUGccNJaQscayBn+s+pfTDw2iYcPbrUkKFZ1q1apRvVlNjh0/RRPvBiQnJhGdFEWSiOeZug9jp9YSklaFj994lwXLl9ha3DLn56nTMV93o7b7zWRL1YmOv8qXn33Dt1O+yu7n6OhIvwf7sPfvgzT2bIlGpSE+PY7rmkt8PeIT2wivUCmRhkxMkSVyPowtpLpifuaIIt3FCyE8sVgXagKJwF9CiOFSygUAUsp3gHeEEG8BLwIfZLXvAxoJIRoAc4UQ66SU1k+Tm4XiY1AAJ0+epKraI1spAEtoXRP7IP7duMVmcmVkZHDw4EHOnDmjpKzFche7f//+UsvS983UbwnuF8I/cRuYe2kJEeqrPHtfP+zUliyRfs4epEUnkJKSUirrFYfr16+zf//+Mq9GuXXDNoLcct+d+blU48CeQ3n6vv7269w/pjt7Mrfyb9JaEqpGMG3+L/j7+5eVuAr5cPHiRQ4cOEBaWsWNwMiFBGlWFfsoAteBnEVcqpK1HVAEegFhUsobUkoDsAzokE+/P4BH8pyalGeANKBxEdcrFRSLQQE4OTmRYc6b+S3DbMDZtWy9qm+yfs06vvvkawKEN5noMXmqmPLrTwQFBRU+uBLy+8xZLP99PnUcPIgzpmMfXIWvp00tkQ+Ag4MDb3/4Dm9/+A6vv/gKNS6a8Ha8NZ+UEr3ZWKJ00sUlIyODN8e/xtXjF/FSuxFpiKPHQ32YOOn1MtlHdnCwx5BswF6jzm4zSxOafApnqVQqXnjpeV546Xmry6VQOPHx8Tz11NNcvnwNKQVqtWTixFd44omKHyll5aiEA0CIEKImEI7FeXBYEcdeBdoJIZywbCX0BA4CCCFCpJQ3izUMIiuhQtY616SURiFEdaAecLmUzqVIKIpBAdSrV490NxXhKbEEufoAoDNkcCTzGi8/kJ+PiXW5du0a33/wNQ/5dcc+y4oRkRLDhGfHs3jV35XcwSgv+/fv59/f/2Ri7faos8IID8dc5ZO33uXrn38slTWGPPkEX497h2Eu3qhVlovh8djLNGrbIo/DXVnw/ZffIk4l80hAN8CipKxf9h+rGzXggWJmWLwbho9+nBmf/UFd9zbZn7fLKSd56OnKvW9fGXjxxfFcuhSBs5Mlj4TZbGby5G9o3rx5hXaoFloHNCXyMTha4LNZF+gXgQ1YogpmSylPCSGey3p+uhAiAMsF3w0wCyFeARpKKfcJIf4GDgNG4Ai3QhEnCyHqYQlXvAI8l9XeCZiU5axoBl6QUsaW4ATvGkUxKAAhBN/N/ImJz76EfcRF7FUaIs0pvPnVhzZJTrRq+Soa29XKVgoAAl39OHrjPGFhYaUag14RWDpvIX38amcrBQAt/YLZcmQ3GRkZpXLhvu+++xj43DBmzpxLsJ0nCUYdniFV+fqzj0o8d3HYtm4LT/jfypMghKCDT1P+mvtnmSgGjz72Py6cv8j6FVtxxpN0kmjbqwXPj3vW6msrFJ/4+HjOnDmXrRSAxaIjzXbMn7+QyZM/t6F0JUMaMjBGXrLuGlKuBdbe1jY9x99RWLYY8hv7AVm+A7e159k6yGqfD8wvibwlRVEMCqFatWosXvMPFy5cID09nYYNG9rEhAygS03DTuRd205oy6R8c3kjXafL5f9xE62wFKcqrTv6kU+N4uEh/+P8+fP4+PjYNKRNmmUuL38AB7Ud6br0MllfpVLx7gdv88JLz3H58mWqVq16z2bwrEjo9XpEPj50KrWa1NRUG0hUmihVEksbxfmwCAghqFu3Ls2aNbOZUgBwf//enNNfyeVwqDNkkKBKsVpJ3vJMj4H92XPjaq62iJREHP29SyWdb05cXFxo2bKlzePc6zVtwOXE8FxtR+POcf+gvmUqh5eXFy1btlSUggqCv78/Hp5u6PW5SzGbTBk8+GAF3waSFh+D4h4KeVEsBhWIpk2bct/AjqxavZ0QbVUypIFzxqt88P0nqNXqwieoZAwc9ABb165nwbkjNHby5oZexxFTIt/Onln44ArKWx+/y7NPjCE8OhZvtRvXjbGYqtozYtRIW4umUI4RQjBlyvc89dTTJKekgQS1RtK9e0d69Ohha/FKhKXssnKPW5qIyhru1rp1a3nw4MHCO1ZATp8+zdaNW3DzcKf/wP74+PjYWiSbYTabOXDgAAd378E/KIi+/fuVeR7+siY9PZ0N6zdwLfQKTVs3p3PnzpWmCqKCdUlKSmLVqlXExNygW7eutGjRosycloUQhwrJF1Asmgd6yc3P9Cn2eN+PF1lFroqMohgoKCgoKFgdaykGzXyd5YYHix/mX2XWfkUxuA1lK0FBQUFBoQKj+AqUNopikA9ms5ldu3Zx+sQpatWpRdNmzdi6eQu6NB3de/W458ICFYqOXq9n69atXL50mWYtm9G2bVvFzK+gYE2k9csu32soisFt6HQ6xg5/CruoTKqqvFiXuJjTkWH0rNkRB409y39dQp9hA3lxwnhbi6pQzoiJieGpx5/CLdkVd+HGljmbcKrtwsy5M7G3t7e1eAoKlRYlXPHOCCGSsdR7kFiqO6Zzq9aDs5Qyj+e6ohjcxu8zfyMgWkPbgKYYzSbWn9/LUM+e2JudCfILookMYfkfa+g9oO89WV1P4c58+t6nhKTXItjHEtJYlxCOXDzKgrkLGDN2jI2lU1CopGjt0QTWLsEEe0pNlPKIlDI7n7sQ4rCUsmXOx/mNURSD29i8egNDfNsAcC05mgCNFx72bsSlWkrxqoSKeppqbF6/SVEMFHJx8tAJBvr0z9XW0LMha/5ZoygGCgpWQhoyMYSXTgG1ewEhhCZHGed8E/MoisFtaO206E1GNCoNWpUGgzQCEnKE9BikEQfHss+Tr1C+ESqBRObKMGcw621SU0FB4Z5BClC2EorKDuBvIcQ6oDNwOr9OilfUbTz0xBD+u3ESKSWBrj4kyTSup8fg5m6xxmQa9ZwxXaXfwP6FzKRwr9G1TzfOxJ/Nfiyl5GjCMYaMGGJDqRQUKj9K5sMi8zqwDmiCpejTk/l1UiwGt/HY449x7uRpFm7dRpDGE7cqnqyPPURzVUPsb1wn3BzLxI/fpEqVKkWa7+rVq1y/fp2QkBB8fX2tLL1CSYiOjubSpUsEBwdTtWq+9VAK5LW3XmPCtQlsOf0v7rgRK+PoMqgrgx8abAVpFRTyx2w2c/LkSTIyMmjWrFmld3wVdvZogmqWYIadpSZLeUQI4YmliFOXrKb/gPeklAl3HGOLBEdCCA9gFtAYi3fkU8A5YDFQA0vt6SE3BRdCvAWMAUzAeCnlhsLW8HRzlz9N/YlhI4cXK7NXZGQkoaGhVKtWjapVq3L8+HHS09Np3rw5jo6OhY7PyMjgjZcmEHUyFH+tG1cy4+kwoAdvvvf2PVceubxjNpv54v33ObNjB3WdnQlL1+HbuAmffvcddnZ5izQVRlhYGBEREYSEhCi1BBTKlNDQUJ596kUMSRpUaMjUJPDJ5Pfo0dP2aY+tleCoqberXN2vZeEd70D1hTsqdYIjIcRyLNaBBVlNI4CWUsqH7jjGRorBXGCnlHKWEMIOSwjF20C8lHKyEGIS4CmlfFMI0RD4E2gDBAKbgbpSSlNBa4R4VZE967ek1RMDeWbc89Y9oXz48uPPSdp8ivYB9QGLWXnltQMMfGMMDz78YJnLo3BnFi1YwKk5vzOyXt1spW1FaCj2Pe/nxYkTbSydgkLRMJvN9OkxELeUurg4WMorG0yZhGXuZuXGv2xusbSmYrCqT6tij6/x5/bKrhgck1I2K6wtJ2XuYyCEcMNi0vgNQEqpl1ImAoOBuVnd5gIPZv09GFgkpcyUUoYBF7EoCQWiEoJHgluwYuESjEZjYd1Lne3rN9PW/1bUghCC7v6NWDb/zzKXRaFg1ixezMO1auay5PSvXp1ta9bYUCoFhbvj5MmTGJI02UoBgFZtj5M+kDWr19pQMiujVFcsjDQhRLebD4QQ3YG0ggbYwsegFnAD+F0I0Qw4BLwM+EspIwGklJFCiJs22CBgb47x17Pa8iCEGAuMBfBzcketUuGmtiMlJQVPT8/8hlgNaTLnqX9ur9aSkZ5RpnJYm9jYWBbMWcDJwyeo06AOI8c8SWBgoK3Fuiv0+kzsb6tOqVGpkKYCjVIKlZjU1FQWLVzMru27qVotiJFPjyAkJKTQcWazmY0bNrLqr5Wo1CoeHvYI3bp1K5Ptw4yMDETeXDWoUKPTpVt9fVshEUp1xYJ5BpgnhLhZbS8ey3bCHbGFYqABWgIvSSn3CSGmAJMK6J/fNyrf/Q8p5UxgJkA970CZaTSQIoy4u7uXVOa7pkHLJly4EEFdr1s6zP6Yi9z/ROWJZoiIiGDMkNGEGKtRzzWQqMtXGLV6BD/Nn1ahcjx06t2Hbf9upXeN6tltByKjadymUMOUQiUkJSWFYQ8PxznOgyCn6kRdSGTspuf5ZMqHdOrcqcCx7775Lue3nqWBaz2klEyZ+D0HHtnPG++8aXW5mzVrhl6biMGUiVZtcTiU0kyaJpI+fe+3+vq2QtjZoalaw9ZilFuklKeAVkIIFyzuAymFjbGFYnAduC6l3Jf1+G8sikG0EKJKlrWgChCTo3+1HOOrAhGFLWI0mZh3ZT9jXh9nk1z1b374Ls8+MZorEbH4aVy5YkjAEOTMR6NGlrks1uKXH36mibk2tX1qAOBm74p7qitff/wVvy6YZVvh7oKnnnuOF/fsIeL8Beo7OXEpPYOzajU/TSpIX1WorCxauBinWA/qezUBwNXeDS+DD1988CWrt3S8493/+fPnOf7vUe73v+Xo182pM+uXb2L46BFWt6TZ29vz6Zfv8/bEj3DSByJQo9NEMnTUYGrXLklmwPKN1OvRX7tiazHKLUKID257DICU8qM7jSlzxUBKGSWEuCaEqCelPAf0xJJk4TSWmMrJWf+vyBqyEvhDCPEdFufDEGB/Yeskq4yM++ZDOnXubI3TKBR/f3+WrF3O5k2buXIplG4tm9OhQ4dKVVDnyP7DDPTsmqvN38WXXReP2Eii4uHs7MysxYvZuWMH506epF1ICG/37IlWm29SsHsCk8nErl27uHLlKk2bNqFp06aVLppGSsmhQ4c4c+YstWvXol27dqhUKnZt20WwS+7wN0etE/pkAykpKbi5ueU73+HDh/E353bwE0LgL/04fvx4mWyxde/RnVWbmrBmzTp0aTr69L3/nij6do/4ChSXnBYCZ2AQkG8q5JvYKo/BS8DCrIiEUGA0FkfIJUKIMcBV4FGwmEGEEEuwKA5GYFxhEQkA1WvVtJlScBM7Ozv6D6g8Wwe34+XjTXJiKh4Ot34oM4yZODhXvEx/arWabt270617d1uLYnPi4uJ48vExGGM02BucmWf/F9Wb+DNt1s+VRlnKyMhgzMixRJ5Pwl7vjsEuFdeqaub+8RuBVQOJvZSIs51Ldn8pzRiFocBQ5YCAAHTqvHv5OrWuTMNWfXx8ePLJAreQKx2KYnBnpJTf5XwshPgSWFXQGJvcvkopj0opW0spm0opH5RSJkgp46SUPaWUIVn/x+fo/5mUsraUsp6Ucp0tZFbIy5hxT/Nf/EH0JgMAJrOJ/2IP8sTTlWe75F7kk/c/w/lGEA3c7qOWd0Mau3Qg8kgKCxdUnoiaGdN+Jf60mXou7ajh1YAQl/swXnXl68nf8eQzI7lgPEmGwXKRl9LMyYQj9Bvcp0DFqGPHjiS7pBKZEpXddi3pGmZvaN68ubVP6Z5FypId9xpSSj1wQwiR11M1C5vkMSgLWrduLQ8ePFjm65rNZq5cuYKzs3OlTW4jpeT69euoVCr27t7Lr1NmoDWoyVQZeGzUUJ4aO6bSmZ3vJTq16k5rpz653sNMYwYRHidYvvZvG0pWevTpPpDqmW1Rq24ZTaU0c8b0L9v3bmb7tu1M/vArTGlm9GTSb3AfJk6aiEZTsJE1PDycdya+TcSlCCSSmg1q8tm3n9s8h0B5wGp5DPx95crH75irp1BqTvm1sucxqA5MATpgcebfA7wopbx6pzFKSuRSZN/evXwy6UNcDXakm/T41Argq5++w8vLy9ailRoXL17kjRdfQyYakUi03g78Mm863t7euLi4oFbfUQlVqFBIcgYEmaUJbSEXxYqESiUwSzM5P60Sme0D1LVbV7r824Xk5GQcHR2LnAEzKCiIOYvmkpaWhhACJycnK0ivkBOzXk/mtTte4xRgNvA78EjW48ez2nrdaUDl8YSzMTExMXww/m0GObfhAb8ODKnSjeqRLrz63Hhbi1Zq6PV6Xho9jjbGBvTz7UJ/3640T6/DS0+Nw9nZWVEKKgmde3TgWvKl7MdSSkJTTzFk+CMFjKpYPDzkQa6knMrVdi3lHL0H3PqtFELg7u5erLTYzs7OilJQhpilKPZxD+AtpVwgpTRlHQsAn4IGKIpBKbFy2Qoaa4Jxsbv1Y1Dboyqp1+K5erVyaLM7d+7EP9MDL8dbyaJ8nb3xSndh//5CA0UUKghvvz8J+5BMTqbs4kLiMY6mbaNV3/o88r/KoxiMHvMkDboEcTptO6FJxzmr20VAMwcmTKw8ivw9gwRpFsU+7gFuCCFGCSE0WcdoLEkG70jlsQ3amIS4eFy1eT2WXVSOJCUl2UCi0icxMRGVQbD+/HYuxlqUnXp+NXB2diIxMRGj0cis6b+y6u9VGA0G2nRsy4RJr1b4rZTQ0FCmfP4ll89dxNHFmaHPjGLwQw/ZzI9i86bNzJwyneSEZHwD/fDx8+b88TMIIegx4H5eePlFHByKHxni6urKwr/mcebMGcLDw6lXrx7BwcGleAZ3h9FoZOYvM1n59ypMRhPtOrdlwpsTSvS50mg0/PjL94SGhnLp0iWqV69eoZJy3QmDwcBv039l7dIVmIwm2nRuz/g3Xi3zzK9libCzx65aDVuLUZ4ZjcXH4Ess+4O7struiOJ8WErs27ePKeM/Y2Bgh+y2TKOexbHbWbl9XbHMkeWNa9eu0bl5Z+qrmlPN3hIbfSXzAuflSfad2ssPX35H5M6rtPRuilql4mJCGGGukSxZ/VeFPf/IyEie/d8w/udZl5oevqTqM1h6/QTdxg5j+Kh8S5lblXWr1/Lz+1Po7tMOJ60jvx/6k1oaH+5v3BGtvT1HbpwlrbYdM+f/VuayWYvXX36d6/9F0NizCSqhIiwxlGjPSP6qwJ8ra/HG+FfJPBROR7+GqFUqTsVd5rjDDf5ctdTmoabWcj5s5OEul3QtOCNlQTReubZQuYQQfbFcXNXALCnl5Nuer49lH78l8I6U8pscz3lwWzVhKeUeIcSjwIdAA6CNlPJgVn9vLIn/7gPmSClfLPbJFRNlK6GUaNOmDYFtarMuYg+XEyM4GxfK35HbefmdiZXmxysmJgZ/52A81P4YzUaMZiNe6kD8XII4e/Ysx3Yepa1fS7RqDSqhoq5XbdwTHdm8abOtRS82f/w+jx5OQdT0sHiVu9g5MKx6S5bMmovJBrUUpn//C718O+Ji58zVpHD8VK60dqtP3I04NCo19/k3IvliDKdPny5z2axBREQEx3edoLl3CzQqy+eqtmcdHOKd2Lp1q63FK1dERERwcf9JulVpmv0dbOJTC78UO/6t1K9V8QsoFSX/QVZY389AP6Ah8HhW1d+cxAPjgW/IyxRgvZSyPtAMOJPVfhJ4GNhxW/8M4D3gtaKdf6HyLxVC1Mr6e5oQ4niWUnJHKvVWQlhYGIvm/kFMZDSdenXhgcGDrHaRFkLw1ZRv2blzJ1vWbMTNsypTH5tk06xjSUlJ/L1oCacOH6N2g7oMeeLxEoVNhYWFUc29JoHOQSQmJCIQBHn6YtDpOHz4MN5qjzxjfNReXDhzHspZoicpJbt27WL90n9QqQQDhjxK27Zt8/S7dPoMfd1zv2ZatRon1KSmppaoDsfx48dZsmAJaalp9HuwH7169So0M2Z6ig5HP8s2QawuHn+NJ1qVhuSMW8XSfHHjypUrNGx4+29X6ZCUlMTiP5dw7OBx6jWqy7ARj+PjU6AvU7G5evUqHqq8ZnAPPDh/5gJ9+/a1yroVkStXrlBFm/fzWEXjzsWzF+jdp48NpCobrOxE2Aa4KKUMBRBCLMJS9Tdb+5ZSxgAxQogBOQfmqCY8KqufHtBn/X0mq0+uxaSUacB/Qog6pSR/iJQyVAhxH1Ab6ANsBP6604BKazFITUll3JAxZGy9SvUwOzZ99xdPDR1JZmam1dZUqVR07dqVj7/6jNfeesOmSkFMTAwjHhxC6MJtNLhux41/DjH6waGEhoYWe846deqgs0vCycmJwKBAqgRVwdHJkVRtIm3btiXWFM/tW1PRplgaNLXOBaokfP3JZyx460OaXY6j0aUb/PrqW/z03fd5+tVr2piLiTG52vQmIzphwtXVtdjrL5i3gNeffJ20/1KxO6FlxlszeO2liXlev9tx9nAlTa8DwM/ZhwhDHHqTAQfHWz4F0SRaLTd+TEwMQx54jH9n7EZz2pX984/z2APDuHz5slXWq1mzJgnm+Dzt8cTRsEkDq6xZUalZsybh+oQ87dcNCdRrVN8GEpUdVi67HARcy/H4jhV+8yFnNeEjQohZQgjnuzu7UmMgsCSrirGxoI6VVjGIiojkYf9O1Pepgb+LN12rNMcl0syqFSttLVqZMOPHn2knq9IuoD4Bzp608q9DP9eGfPfpl8Wes1mzZvjV8+Rc/AlMZiNGk4Ez8Uep0TSIDh060KZXe3bFHCDDmInJbOJU3DkyfE10L2dphi9fvsyxDVt4pk4zanv6EOLly3N1W7Dj7+VERUXl6vv4qJFs10dxJi4CKSUJGWnMCzvIiBfGFrvuRWpqKrN/nE1P3x5UdauKn7MfHXzbc2nfJY4cKbjOxIuvj2dz7C4SMpKo5hZIvExjb/IpPHw80ZsM7Io8hn9j6znSTf3uJwLTalHPqxGejl7U8axHXXMTJn9c/M9VQfj7+9Om530ciN2P3qTHZDZxNv4sZn8z3bp1s8qaFZWAgACadW/LxvBDZBgtr9WhmPMkeYtKnepb2NlhH1y92AfgI4Q4mOMYe/sS+SxbVOe8m9WEp0kpWwBpFFxN2BpsFkIcwuJwuCzLilGgR3yl3UoQEpztckcJ1Herzs5N2/nfkAK3VyoFB3ft4ynf9rnagt38WHvm32LPKYRg+uxpzJz2K+tWrEeoBAPHDODpZ8cA8MFnH/JH/YUsXfg3ep2ezgO68NxDD7Bu3Tp8fHyyi9TYmoMHDtDcwTWXCU8lBE0c3Tly5Aj9+vXLbvfz8+OnhXOY/t0U1h7Zj4eXJ8M/foP7e/cu9vqnT5/GV+2LWpU770OgCGTX9l20bNnyjmN73t8TR2dHZnw/nRtR0bTs1x6/QD9W7zmIMKroN2IAo58ZU2zZCmP/7gO0c899kfF3qcKO0xustubHX3zM/PrzWfrHMvSZero92I0Xxj9faBbCe5EPPv+EBXPnsXTRMgyZejr37cGvLz1fqXOMmDP1ZFwpUUh4bCHOh8Wq8JtjbH7VhMsMKeWrQoimwDUp5U2TUreCxlTab5ZZSqSUuX78kzJS8A8sqgWoYuPh6UFSpg5Ph1uFYDKNerSO9iWa18HBgfETXmL8hJfyPKdSqRj+5AiGPzkCKSWfvv8xb46eSKD0RafJ5Cu3TKbPm0lAQECJZCgp3j4+HM6nDlei2ZBvCFy1atX47Pv8fIqKh5eXF+kyb7GdNKnDL6DwNNodOnSgQ4cOhfazBh6e7ujidbkKDOlNeuwdrOdgq1KpeHL0kzw5uuyjQCoaKpWKkaNHMXL0KFuLUqZY2cfgABAihKgJhANDgWFFGVhANeEyQwhxswSuZ87roZRyuxCilZTy0O1jbH/7ZiWcXJ05dONs9p6tzpDBPt15HhtZpPezwjP8uadYF3UUo9lyATRLMxsij/O/kY+Xyfpbtmzh+LrDDPTrSauAZnT2aUPjjNq88+pbZbJ+QXTs2JFz6LmadGs/NjQxjmv2gvvuu8/q69epUweXqi5cTbp1l5OUkUSEJoJ+A/oVMNL2jH5uFMeTD2DK+lxJaeZ44kGeeOre+F4plEesG5UgpTQCLwIbsEQULMmq+vucEOI5ACFEgBDiOvAq8K4Q4nqWyR5uVRM+DjQHPs8a81DWmPbAGiFEttlNCHEZ+A4YlTVXSRy1JuZz3Ix4yLcMZ6W1GAQGBeLQNpAF/23BWe1AutbIG1+9ZzWnrPLG/b17cyMqhlnTZ+OuciTJlE7/IQ/yRBmVY12x6B+aujbIZbGp6hbIwYsnSUtLw9nZVv43lnLY3/0+iw8mTMR4IQyTlDgHBfDD7FllttXx06yfePOVN9l4ZiNqocHO044ff/kRNze3wgfbkL79+hITFcPv0+biiDPpMo2Hn3yQJ0bcWTFITU0lJSUFf3//crGVpFD6mM1moqOjcXd3x8nJidTUVFJTU/Hz8yuT99zaZZellGuBtbe1Tc/xdxSWLYb8xh4F8mxVSCn/Af65w5gaxZc2z1yDbm/LKqyElPKV/MZU+gRHaWlppKSklNkHtLxhMBiIjY3Fy8sLe/uSbSPcDS89PQ6/S674Onvnal8es5Gl/y63qWKQk7i4OIQQNsvOmJSURGZmJr6+vhWqImVRPlfp6el89PYHHNl1GCe1A5l2RiZ99BZdunXNt79CxWTdmvV89/n3aA32pBqTERozdmYNzhon9PZG3v70XTp17mS9BEe+/nLxQ8W3hDb5dUplr64YAjwA5Ayjeg6YDmyTUm6/fUyltRjcxNnZudxchGyBVqulSpUqZb7u4KEPMWPSVHo4dcy+4F1PjiCoTtVy9X54e3sX3smKlCQPgi0pyufqw7feJ21fHA/73Y8QgnRDOp+99jHV/ppFzZo1y0hSBWty8uRJvn7nWzp4dsfO0Y4t59fimelIkyr1CfQPJN2QzscTPuDXpbOtJoNZryf9yrXCO967/I3FMpGco80IpJKVU+F2Kr1ioGAbevbsyZ5+u1i9fiuB0oc0dSY690ymfzfT1qIplAEpKSkc232Eh7KUAgBHrSPN7OuxaN6fvPXB2zaWUKE0mPfrPOrbN8FObUeGMYOk1Di6ePciJTkZGShx1DrSRGt5z62GtP5WQgXHJKX8MGeDEGK4lPLbOw0okmIghGgNdAYCgXQsqRw3SynzZh5RUMAS2vjeJx9w9ZmrHD16FF9fX9q2bVvq2znXr19n6tc/cOLICTw8PRj1whh69yl+KGF+XLt2jR+//pHjh4/j4eXBmBfH0LsE4Yr3AikpKTipHfNsj7jZu3AjKuYOoxQqGlGRMQTY1wBAb8y0vOdZ/25GhbnauRATGW1FKe6Z8snFJW8ImSV98x0pUDEQQozKmiAMOAScAxyATsCbQoiTwHtSyspRV1ih1AkODrZaZb6YmBieeWw0bdT1GOLRnZTMNKa/8z2JcfEMGTa01NYY89gY6psb0NOtF2nJafzw5g8kxCbw2LDHSmWNykhAQAAZWgPphgwctbeyMl5KvcrgXsrrVlno3rsrG37+l4beTXGxdyXFnIbOpEOlEdk3AaG6qzx8/1C+m/aDVWQQ9locqlcrvOM9hhDiRynleCnlrhxtHYAxQHcsWRnzpTCLgTPQUcp8gq4tizQHQgBFMVAocxbOWUBjqlPL0/Kj4GbvQv+ATsz+eRb/GzqkVKwT83+fT3VDDap5WdZwsXehs08XZv00i0eHPnpPOrQWBZVKxZsfT+KL1z6hqX1d3O3duJh2BWOwmv4PDCh8AoUKwWPDHmPVP6s5GXGUQKdqBHvXYnn0evrU7saNtDgu6a4ga2job8VaKTLTQPrl61abvwLTVwjxP2An8ASW0MTLWKpA3p7dMRcFKgZSyp8Lef7o3UipoFCanD52kqauuRNWadUaHMxaUlJSSsWx79TRU1Rzzm3x0Kg0aIyaUlujstK1WzeqLQlm0bw/uBF1g0G9hjBg0MBKU21UAZycnFj49wJW/LOCXdt20yW4A291e53tm7cRG3WDB+8fwoAHBlj1PZcoPgZ3oD+WKo1zgUTgcSnl7ZUc86WoPgY1sexT1Mg5Jr/4SAWFsqJuo3qEr72Em/2tLHxGs5EMYcDFxaWAkUWnfuP6hF4KxcX+VqEzo9mIQV16a1RmatWqxdsfvmtrMRSsiKOjI0OHDWVoju27Tp06lakMio9BXqSUF4EnhRAvYcnW+JUQwozFYrBISplyp7FFtYMux2KCmAp8m+NQULAZw0eP5KjpEuHJFscmnSGDDVF7GPb0iFLLDT/iqRFcFBeJSrUUV0o3pLM7djcjx46s1PnnFRQqEhJR7KOyI6VMllLOlFK2A54G6gLHCxpT1HDFDCnljyUV0NYkJSVx8OBBnJ2dad26tdWLsERERDB/zkIunrtE63YtefyJoTbNbHfkyBH+mreItNQ0+jzYnz59+9js4mY0Glm3Zi1b12zA1cOdR0c8TpMmTe5qjipVqvDLwl/5/vNv2HlmM86uzox4+2kGPTj4ruVJSEjgzwWLOHbwGHUb1WPEqCfw8/OjSpUqzPxzJt98+g0bz2zExc2ZMe89xeCH7n6NwoiKiuLEiRP4+vpaTLRz/iA6Mobuvbvy4MMPlmmCKoWKy+HDh/lj7iJ0aek88MgA+vTpXal9YVR2djjWUJwPi4KU8jTwuhCiwEJORcp8KIQYhsXJcCOQmWORwyWU02rczHx4k7/+XMS8KdNoYOdNujBxTZXBt7N+sVqK5LNnzzJ2+Av4Gurg7uBNrC6SDK8Y/lw23yZJdRbMmc9fP82njWsDHDR2nEi+hFvzQKZMn1rmGffMZjMvjnkW1flYWngEk2bIYHviRYZNfI5HHiv7ypfR0dEMf+RJfNKC8HWsQlz6DSLtQpn15wxq1bqj426pIaXkm8nfsu6vDXhIf+J0MVyMPkuPGn3wcfLjevpl1NXNzF08R1EOFArk91lzmPPjIqpp66NVaQnPvES9DtX4cdoPNs+saa3Mh/VdveXM1n2KPb7rtj8rdebD4lBUNbIJ8AwwmVvbCKVXbs7KhIWF8ecP03mpeif6VW3Mw0HNeMytHpNeeBlrpYT+5L3PqSVaU9W9Nq72HtT0bIBLQhAzfi77BD+pqanM/2U2Dwd2pbp7FfydvelVpQ2xR6+SU3kqK3bu3InxfDT9qzaniosXdTwDGRncgdk//EJGRkaZyzP1u58ITK9Fbc8GuDl4UNMzhBCa88VHX5bJ+tu3b2fzoh20d+9NQ49m+KfUpIW2E+djzuLh6EljrxYYrwhWrVxVJvIoVEySk5OZ/cs8mrt3xde5Ch6OPjTyaMuZvWEcOXLE1uJZDYnFx6C4h0JeiqoYPATUklJ2lVJ2zzp6WFOw0mTdylV0cKmKNofZPMDZA1edmQsXLlhlzauh13F3zG0ZCHSryc5tu62yXkGcOXOGIK0PalXubYOa2gD2bN91h1HWY9/2XTRw9M/VplVrCNK6F+v9kFKSlJSEXp9vds9CObD7IFXdauRq83X25+LZ0GLNd7f8teBvajtaCk4ZjEaEFPjZBZKkS8JgMgAQ5BDM9k1FcihWuAcxm83s27cPV7M3KpH7Z93D7M+uHWX/u1OWWLO64r1IUTfZjwEeQIVMWWbUG1DnowOpERiNRqusqbXXYDQb0ahuvcRp+mT8qvlaZb2C8Pb2JsWcNxVFsklH06CAMpfHNzCAUMOpPO2JRt1db7Ps2L6Drz78EqmTZMgM7n+gN6+99dpd+Y94+niRdiMNF7tbNUb0Jj2OTmVjtjcYjNgJi9KmVqkxSzMAKlRILBatFH0ydaoq+6gKeflr0V/M+PFXDKkmLoZdxFxFRX3/FtlbBxlSx//bO+/4pqo2AD8nSffeu7TsvQVkTxVUhqg4EXChCLgX6ufEgQoiIKIMBZQlCAKKoCiyEWSWVVbp3ntlnO+PhNJFW9qkKe19+N1fyblnvDk3yX3vOe/wt8L3vDZRnvyvjRAiFBgKtAIcgDhgF/CnlFJfXpuqrhj4AaeEEFuEEBuuHOYQuja45c7b2ZsTg6HYtkFafg5JGi0tW7a0yJj3jb2Hs5mHkKYfeZ1By7m8/3h80gSLjFcRjRs3xinEndOpF4vKUvLSiRTxDLVg4JFrMeKukfynjSM176q3zJHki3g3DyUwMLDK/Zw5c4b3n3+XXpru3OI1kDu8biNiwzE+/fD6drkefXocxzMPoDcYlUSDNHA8/QAPTrh2KmFzMmrMCC7kngJApVbh5OJEYkEsDnb22KptydPmckGe5oGx1c8gp1A/2bZtGws+XEQ3mwH08x5KX4/biI2/wLlko+KdmZ9OtlMiQ4feZmVJLYfK1hansOBqH/UVIUQzIcRPwFIgCDgE/AEkAfcDh4QQU8trW9XHqv+ZQ1Br0bJlSwY9NJp5y1bTwdaHXHSc0Kfx/rxZFrPWfeyJR8nJzmXdyg3YSAf0mnymvPVUrfv3XmHWgjm88eJr/HhsGzYqDbaejsxcNMcqXhKenp5Mnz+L9195E5LzyTdoadqpDR9/Mv26+ln67fe0s2uDo40jACqhopN3Bzb9soXnX3m+ykFVhgwZQtJLSXw7dxG2BgfyZS73PnY3DzxUOzfiW2+7le1b/2L/39tx1/mS75TD2YLjeLp7sCdzO8IJ3vviHcLCwmpFHoUbh2+/XEg7l25o1DYANA4Ph0uwI/ZXsh2TcPN1YsEXc+t1zA19YSHZF2OsLUZdxA94WUp5rryTQggVUO7TT4VeCUIIISuxzqtKHWtQ2isBjAl3dv6zExcXZwYMHIijo6PF5SgsLCQjIwNPT8864feenZ1NYWEhnp6e1hYFKSWpqanY29tXKxXzYw8+SmhcEG72JZWb31P+5Ifff7zuqIQ6nY60tDTc3NzMEqmtsLCQpUuW8suaXxAqwagxo3jg4Qeuuc1x6tQpDv57CP8AP/r06YNOpyM317i9Yg6L8oSEBObOmseBPf/i4+vDE1Mes5qiag0KCwtZungpG9f+ghCCUffdxQMPP1AnvpfV5Zbet9HdZlCZz8eOrM2s3bKmTnzPr2Apr4TmLl5yTqfqh9m+9Z+lDc4rQQgxVEr567XOV7ZisN20FLG+eKIkIYQtxkRKjwDbgSVmkNXiBAcHc9/95kmuU1VsbW3x8al9u4JrUZeeHIQQNXLdvLlfT3Z/s4P29lfjH+RqcxGOolorIRqNxmzXSkrJM088Q9bRHLp4dEPqJBu/3Mx///7HzLkzy23TsmXLEltbtra2ZlNeU1JSeOiusQTkNaGTaz+yLmfy1tPvMfWdpy0Sk6GuIaXkmcefIfd4Dt08u2GQBjZ9uYn//v2Pz+d8bm3xqk2nmzoSu+MyQW5Xw3Yn5STQrHWzOqUUWBbFiLAihBDlrfhPFELMAn6WUp4ufbIyxeA2YALwoykscjrG7IpqjDENZir5EhSsxZgHxrBx7S8cTTpGI5dGpOenc7wggrdmvW11n+3jx48Tdyye3t59i8q6eHfl733bOXfunMXiZ1yLpUuW4Z0bSoi7MS6Dq707XW36MefTedw54s56HQAH4NixYyREJNDXp09R2U0+Xflz35+cP3++VuJVWIKpL03h4b3jKEjLx9cpgJTcBKI0kXzz5nxri1ZrqGxtamYrsNN8stRRygt9rMN4L18BdCp9sjLFYC0wSUo5TwhhA3gDeVLK9BoKqqBQY5ydnVm6ZhmrVqxi9/ZdBAYHMvexr2jWrJm1RePkyZO46z3KlHsYPDl9+nStKwb/HTiMn2OjEmU2alvIU5OdnW3ViJxXOHnyJFt+2YxKwLBRI2jatGnljapIREQEnrqy18PT4MWZM2duWMUgMDCQFRuWs3TJco4dOkbrNs35cMJb12XEey0iIiL45edNqNQqRo027/UwJ/oCLVkXFBuDayGlLLMkJoS4XUr5jhCi3HxHlSkGS4AtQoglwAwpZVyNpVRQMCPOzs5MeGwCEx6rfW+PimjUqBE5muwy5VnqLEJCat/tsFmLppw5E4OT7dWtJL1Bj05VWC37DnOzYM48/lj2Ez2cQ9BKA6+s2cjoSY/ywNiHzNJ/WFgYWde4HsHBN7Zluo+PD8+/9KxZ+5zz5TyWffMTzjIIKSVrlv/CMy8+zsNjHzTrOOZC2Uq4NkKIRuUUX/nBvLu8NhWuH0opV2FcZnAD/hVCvCiEeP7KUSNpFRTqMTfddBPqABVn085ikAYM0sCZtNO4hbnQtm3bWpfnkcfGEqU6TVpeCgBafSFH0vdw//gxVje+i42N5bdla3gsvBftfRvR0S+cJ8J7sWLet6SmpppljG7duoEfnE2LLLoeJ1NP4RruSps2bcwyRn0hOjqaZQtXE+5yM75ujfBzDyPcuSdzZ35DWlqatcUrF0MNjgbAL8BG098/gXPArwBSygvlNajKxqIWyAHsAJdSh0I9QUrJyZMn2bVrFxkZGdYW54ZHpVKxcPlC/Ab4sC1zC9uyfif0lmDmL5lvFfuHkJAQvlo6h+zQOHZl/8pRsZMHX7iHx598rNZlKc3uXbtoo/FCVWxeNCo1rWy9zBay+8r18OzryW/pW9iS8Tsht4Tw1aKvrG6PUtfYvXs3NgUlPWFUKjW2Ok8OHDhgRcmujRL58NpIKdtLKduZ/jYBbsboNHBNKtxKEELcBnwObAA6SylzzSatQp0hOTmZpydMIjcuHwfpSApJPDTxQSY8Nt7aot3QuLq68sEn71tbjCJatWrF96uWWFuMMri4upInygZgy0VvVi8aNzc3ps+4vlgZDRFnZ2dQlxMQT23e62EuVHa2ONfE+HCP+WS5EZBSHhBCVGidWpmNwTTgHill2fi1CvWGl599Bc8Ef9q7G/e+DdLAj3NW0qlLRzp1KmOwqqBgVvr27csc+THd87LxdDDeeBJyMriszjNuASjUKv379+cDp0/JK8zGwWSTkpOfjtqlbl4PfUGhYnxYCUKI9kCmlPKiqWhkRTGIKrMx6KMoBfWbjIwMok5dJtj1qkGcSqhoZteK1ctXF5VJKSksLLRYNkqFhouDgwMfzp/ND5kn+CH6IMsu/8va/Eg+/WbudeW8qCo6nQ69vtwQ8QqAo6Mj87/9ggz7CKLzDnE57yD57hf4dslXFrkeNUcga3DUd4QQi4HvgK1CiIlCCHfgtYoCE9bFq6xQixQWFqIWZY3PbNQ25ObkIaVkyeLv+W7BUgxagYOLLS9Ne57BQwZZQVqF+krbtm35aetmIiMjUalUNGnSxOx7/4mJibz32ltEHjfmpWjdpT1vfPAOHh5l3RgbOu3atWPbX79a9HqYEyWJUoV0A9pitAvcLqWcL4SoMNKjohg0cHx8fHD0diQ9Ow13+6s/kOfzzjJ19CR+WPYD38/8iZYu/VE7aCjIz+ftFz7Ec4kHnTt3tqLkCvUNlUpF8+bNLdK3Xq/nqYcf5WZ9MINCBxuNbU9F8cz4J1m2bmWdvulZC0teD3OisrXBObwGcRv2VV7FZG/3Bcbgft9KKT8qdb4lsBjoDEyTUn5qKrcHdmA03tcAa6SU/zOd6wjMxxhoSAc8LaXcb4os/DXQFaPjxFQp5V/Vf4OcB3yllAlCCI0pR4JDRQ0UxUCBj7/4kKcemYR3ih8OOJAo4mnTrxUDBw7klr7DaObcE7UpfbSdxp5GNh2Z98UCvv2u4URXU7ix2b17N97ZNjQNDAKM4bhbezXibOwBDh8+rNjS3MDoC7Rkno+1WP9CCDUwFxgCRAMHhBAbpJQRxaqlAlOAkaWaFwADpZTZpiCBO4UQv0op9wKfAO9IKX8VQgwzve4PPA4gpWwnhPAFfhVC3CSvpOq9fgqAI0KIXwF/jBkW11TUQFEMagm9Xs+qlatZvXIdKiEY8+DdjB59V50IRdu8eXN++WM9W377ncSERHr2vpm2bdsihCA/txAbx5IJhVztPbhw+ZSVpFVQuH7iYmNxN9iRmpJKRno6AO6eHrhjR0JCgsXGjYqK4pu533DiyHGatWzO4888XmcjCN7IWNhWoBsQKaU8DyCEWAGMAIoUAyllIpAohCiRzcm0j38lspaN6biyty+BKyFH3YAr2k1rjDdvpJSJQoh0jKsH+6sp/wbTAcb4BRFSyuMVNVAUg1piyqTnOLzrIr5OTdEjmfnuEvbt3s+nMz+xtmiA0eBo1F0jy5T7BfmQHZ+Bs93VTIUJWVF069+gkpEp3OC0a9+euTHTCXKzw01jD0BGfDL7s07yvIUCHF24cIEn7nuMNqIVvdy6E7c/non3PcnMxbNo165d5R0oVBkL2xgEAZeLvY4Gule1sWnF4SDQFJgrpbyyefEsxsjCn2J0BOhpKj8CjDApICFAF9PfaikGUsrvTasVLTEqI2WSJpXG+o+rDYBTp07x375ThLi3w87GATsbR0LdO7Drr0NcuFBu4Kk6w+tvv0ykdh+JWdEU6vKJyYgkzekCT0+eaG3RFBSqjJSSLH0e+zLOkaXPI1OXy+6MM+TLQgwGy8S/+3LGbDpq2hPuEYZGpSHELZibnbrx2fufWWS8hoyU1T8AbyHEv8WOJ0p1X57WUWX3LCmlXkrZEQgGugkhroQ+fQp4TkoZAjwHLDSVL8KofPwLzAJ2Y7RBqBZCiC7ASWAe8BVwSghxU0VtlBWDWiAiIgJVgSuUig2iLnQlIiKC8PBw6whWBbp06cJ3axbwzbyFnDt7iu53dOLRJ6bj6+trbdEUFKrMsaNHGRbWCRUqfo89gVqo6BrenFB9MCdPnqRRo/LCydeMU8dPcotLSe8db0cv9kWZJ5qjghGVnQ0ujWtgfPgvyVLKipZAozE+sV8hmKvL/lVGSpkuhPgLY9bi48AjwFTT6dXAt6Z6OoyKAgBCiN3A2esdrxhzgDFSyoOm/joDXwK9rtVAUQxqgYCAAIRtQdkTtgUEBATUvkDXSdOmTfn48w+tLYaCQrUJDgnhb1HAHUGd6BHcsqh8Tcy/ZslEWB4+/r6kJ2bg4eBeVJZTmIuzW92LHngjoy/QknHeovn9DgDNhBDhQAxwH/BAVRoKIXwArUkpcAAGAx+bTscC/YC/gIGYbv5CCEdASClzhBBDAF0pQ8frxeGKUgAgpTwkhKgwc5qylVALdO/eHbcADSlZ0UgpkVKSlBmFT4ijYg2toFAL3HzzzSS5SE6mXv0OHkm6gM7fyWL7/U8+O5E9GfvI1eYBUKArYHfaXh6fXHqlWqFmVD9PQlVyJZie4J8BtmBckl8lpTxhChY0EUAI4S+EiAaeB94QQkQLIVyBAGC7EOIoRgVjq5Ryo6nrx4HPhBBHgOnAlQ+GL3BICHESeAV4uIYTlCuEKNJGTf/Pr6iBqK+R7Lp27SrNlYDFHKSmpvL2m++yb88hQNKzT3fefvdN3NzcKm1b03EvXLhASEiIsvx/HUgpOXPmDPn5+bRu3RobGxuLj5mdnc2ZM2fw9vYmNDTULH2a633o9XoiIiJQq9W0bNmyTnjTXC8pKSl8+Na7nPj3MBJJl949eHzy06SmphIcHGyR78fWLVv5csZsctNzsXO244kpTzCiHCPfhoAQ4mAlS/bVorGTr3y3ZbnZg6vEw4e+sohcdQXTCkSBlFJveq0GnKSUmddsoygGtcuV+bZ0QBWDwcBH73/M7z//gavKiyyZRtc+HZk+44NaucndyERFRTH1iamQJtGgIV2Vzhsfvkm//v0sNubib5ew+KvvcRPe5MlsQlsF8sVXM3FxqX4S06ioKJ57YjJ2mRJboSGBTF6f/hZ9+vW9rn4OHz7Mq5NfwaXQCQnkOeTz2fzPadmyZaVt6yJSSgwGAzPe/5Bdm7cTaOtJgjadNr06887HH1gk7K/BYLghlSlzYinFILyGisHY+q8YeAADKJkR+V3gLeCwlPJImTaKYlA/Wb1yNYs++JH2Hj2KlJBTaf8xcNzNTHluspWlq7tIKRl160ha5bfE18kHgHxdPtvS/mTFryvx8fEx+5gHDhzglUffootHP1Sm8NSXMiMJ6OnMzDnVs2CXUjL61hH0phkBzt4A5GrzWZ2wg2WbV+Pt7V2lfnJzcxkxcDj9nHrjYmdcjUzPz2BP4X5+2b7xhlUyV/24kt+/WMFtgV2Kvh9/xR2hzf39mTj5aStLVz+xlGLQ1DNIfjboqWq3H7nmzfquGOzHGHOh+ArBGGAVsFlK+WvpNg1bha3HLF+yghauHUusTDRza8eGNRsraKVw8uRJVBmqIqUAwF5jTxiN+HVTme+PWVi26AfC7doUKQUAoS5NOLj7PwoKyjFarQInT57EIVsUKQUAjjb2tLEJYfPGzVXuZ8eOHfjrfIuUAgB3ezc8C93Zt68KsWTrKGuWrqCfb9sS349efm3YtGa9FaVSqA76fC3p5+KqfTQANFLKcVLKKVcOIEZKObk8pQAUxaDekp+Xj42qZMRClVCj01bbHbZBkJubiw1ln4JthQ1ZGVkWGTM7KxtbtV2JMiEEaqGhsLCwWn3m5uZiJ8ouidsJDTlZ2eW0uIZs2dloDGX70RjU5ObmVku2ukBBfj526pLXWSPU6HXK9+NGQ2JMKFDdowFQnktZhW5mimJQTxl820AuZp4pURaXFUXn7ooXREW0a9eOFFIo0F29IUspuWS4zKBbLZNRctjIoVzOiyxRlpGfilegR7VtDNq1a0esIY38Uu/jZEEMA2+p+vvo3bs30SIWQ7Ew7TqDjlji6datW7Vkqwv0GTyAI8nnSpSdTo2iY/d6u6Jcr7GkV0I9wFEI8UjxA3AEEELcWV4DJY5BPeXJSU+wa8c4IuIO4ooX2TKdfPd0Pnx1Afn5+djb21tbRADy8/PRaDR1Is+7lBK9Xs9L77zMjDc+IVyEYSNsuKiPov9dAyxmbDd8xJ1sXv8rR4/txlMEkmfIIs0hlq8/m1ftPu3s7Hjh7df4/I0PaWcbiq1KQ0RBNL3uGkyLFi2q3I+/vz/3P3k/q75eSWN1GACR+vM88eKTuLu7V1s+a/PEMxN5fPc40uIPE2TjSbwug8v2WXzz2gfWFk3hOlHb2eDWuAbxYA6bTZS6SpdyygTwHdAK+KXMSWsZH5pcJv7FuNdxhxDCE1gJhAEXgXullGmmuq8BjwJ6YIqUcktl/Td040MArVbLH3/8wbH/ThAcFsS+nfv5d89/qFATGObHh5+9b7Woi2fOnOHtV/5HSnQSemHg5v49ee3t13F0dLSKPBvXb+Drz+ciCiQ6tYHbx4zE1taO3JxchgwdQuvWrS06vsFgYO/evez6ew9BIQHcMfwOXF1dK29YCXFxcWxav5Hc3FwG31b993H+/Hk2r9+E2kbDsDuHWSRSYG2j1Wr5Y9sfnDxynMYtm3HrbbfWGYW5PmIp48MwR1/5ZvMx1W7/2JE59dr4sDpYUzF4HmPGKFeTYvAJkCql/EgI8SrgIaV8RQjRGvgRY4arQGAb0PyKT+a1UBSDkox/6DEyj0GIa1OEEKTlJhFle5Rftq7DyanCIFhmJz09nTFD76a3/U14O3oipeRE6mlU7R34csGcWpUFYOc///DZc+8xMrAX9hpbtHodm+L2ce/L4xl19+hal0dBoT5iScVgWg0UgyfquWIghGgEfIExSZMA9gDPSCmjrtXGKjYGQohg4HZMsaFNjMC4tIHp78hi5SuklAVSygtAJEYlQaGKXLp0iUsRsYS6NSuywvZw9MEx24fNmy1jaV8RG37eQJghGG9HT8BoaNfWqyXnD0cSHx9f6/J8++XX3OLTBXuN0VjTRq3hFr8uLP16Sa3LoqCgcH1IBAZZ/aMBsAhYgzEKoz9GN8VFFTWwlvHhLOBlShqF+kkp4wBMf6+EISsv5WVQeZ0KIZ64kiErKSnJ7ELfqCQmJmJfOoMT4CCcuXwxutblibkUjbumrFGdq9oZa1y3lMRkPOxLyuNoY09+Tl6ty6KgoHD9yBocDQAvKeUyU5ZHvZRyGVBhIJNat/gSQtwBJEopDwoh+lelSTll5V5PKeUCYAEYtxKqK2N9o0WLFmSJFKQ0IMRVXTBTJNKj1yO1Ls9NvbqxeNPXhHE17K/eoCfZkEaTJk1qXZ52nTtw7lA0TT2vJlCLz04hKDykglYKCgp1AY2dDR5NamB8eNR8stRRkoQQ44BlptcPAxU+gVnDFLwXMFwIMQywB1yFEMuABCFEgJQyTggRACSa6psl5WVDxtXVlQfG3cOqBRsIs2+DjdqOyzlnCezgSY8ePWpdngEDBrA07Dv2X/iPVm7NyNXm8W/WUR56ZqxVjA+ffn4yT4wZT15SIeFuAcRkJbEn/wwzZ1ffK0BBQaF20BVoSY2s/S3IG4jxGG0MPsb4oL3LVHZNrBoS2bRi8KLJ+HAGkFLM+NBTSvmyEKIN8ANXjQ//AJopxofXz86dO1m68AdycnK5866h3DX6LquFtC0sLGT1ytVs/WULrm6uPPDoQ1ZRUq6QkJDA9wuXEPHfMZq0bMbYx8ebLZGRgnVJTU0lPj6esLAwHBwcOH/+PCqVirCwMIvnLKmLxMfHk56eTuPGjbG1ta28gZmwlPFhqKOffKXpfdVu/8yx2fXa+LA61CXFwAujUUQoEAXcI6VMNdWbBkwAdMCz1wrjWBxFMVBQaNhotVr+9+pb/PfPv3hoXLmYFY1Br6WpRzB6qUfnpuHjuTNp3LixtUWtFTIzM3l20gtcOHEZW+FAvjqTF994jjuH31Er41tMMXDwky83vb/a7Scf/6JeKwZCiEWUsyUvpbzmqoFVo8pIKf8C/jL9PwUoNySblPIDQIk8oqCgUGW+/Hw2ybsuM9p/CFq9lrNnT3KrezuaOobj4upCQk4qzz76NGu3bqwTAbYszUvPvkrWMTXtXQcAoNNr+eTNWbRo2ZzmzZtbWbqaYVAsyiqieIIcO4wefxXuvdT/b4OCgkKD5LefN3O3zxAAzqRcoIVDAIH23qQkp+Di6oKfkyf+WY7s37+fnj17Wllay5KRkcGpI5F0dL367KVR2xAgmvPj0pX87703rShdzdDY2eDZ1L/6HZwwnyx1ESnl2lJFPwohdlXURlEMFBQU6iVSL1GZvHDydPk4CjtUQmDQXzVPspc2ZGdXPanUjUpOTg42oqw9gZ3GgbTU9NoXyIzoCrSkKMaHVUYI0Qrwq6iOkkRJQUGhXhLWPJzYrAQAGnuEcjI/hhxdPi5uxlDTeoOec7okunatt9vLRQQEBKB2NpCnzSlRnlB4kVvvGGIlqcyDBCXAUQUIITKFEFmmvxnABoxxhK6JohgoKCjUS6a9/ya7C49xKPEE2YU5CAc7VqXsIklkE5F8gR+id3DfU4/g6elpbVEtjhCC9z55m4iCnVxKP0NiVjQR6fsI7eTFLbfc2IoBKGmXK0JK6SqldDH9dZNSNitne6EEylZCA+LQoUN8t3g52dnZjLzrDoYNG4Zarba2WAoKFiEsLIxVv67h55/WceHsBV58/g1CQkPYumkLarWaT0a/fMMb3V0P3bp146dff+Sn1WtJiEtk4i1306dPH1SqG/v5UGNng1eTGtgYRJhPlrqEEMJbSplcnTpWdVe0JIq7YkkWLVzMV7O+w9U2DI3KhoyCaNrfFMbX385rkL7cCgoKtYul3BVDHPzk1PAHqt3+pZOz6qW7ohBiOPA8xrTK24EzQD7GdANdMeYhCpRSDi3d9sZWFRWqRFZWFl/PXUyQexfcnHxwcnAn0L0tRw+e48CBA9YWT0FBQaHaSJSthPKQUm7AePPPB97HuDYSB/wG3Ap8W55SAMpWQoPg1KlT2OBaZKF9BVHgzJzZc5nwWC69evWySBTEyMhIDh48iJ+fH717924Q/uIKCgq1iyw3pY6ClDIDmGs6qozyK90A8Pb2Rk/B1QIpiYmJJTbpEoVZTsw4Po8P3T9h0fJvCAoqN3HldWMwGHjjlTfZv+0/3LQ+FGry+Mh9BguXLzDbGAoKCgqgBDgyN4pi0AAIDw+nUVM/4s9G4+kSTGZmJumpKQhVDr0ajcBOY09ydjyvPv86S1d+Z5Yxt27dyn9bIujmNbCoLCnHvGMoKCgoqO1s8KpJgKNT5pOlvqAoBg2Er7+Zy0svvsbhg/uIjY3DRtgypNmd2GnsAfB28udA5DGys7Nxdnau8XjrVq4nzLFliTIfJ3/2RB412xgKCjUlPz+f+Ph4fH19zZbZU0pJXFwcarUaP78K48gomAFdvpaks0qAI3OiKAYNBDc3NxZ8M4/c3FwmPzkVcdoDDwfvEnUk0myuS2qVCr0sa9ojMdzw7lEKNz5SShbO/4a136/AU+VMqi6bW0bfzpQXn6vR5zMyMpLXp76MITUfCdj7ufDRl58SEhJSaVuF6iIUGwMzo/xCNzAcHR15aMIDXMw/SXFX1fjsGJq0CjfbU9PdD47mfKkx4rKjadLafGMoKFSXTb9sYvvi9YwLGMAo/+6MDxrA8bU7Wf7d0mr3WVBQwNQJT9NL14y7/Psy2r8vnbKDmTzhKQyG+mz/bn2krP6hUBZFMWiA9O/fn0H39ubfrD84nXaE41l7yfCO4qPPzZfAsn///gy4uyd7M7dyMvUwRzL3kOp1iQ8/U5JkKlifH779jiG+HVGbPHVUQsVAv/b8tGxltfvcsWMHQYXu+Dh6FJUFuvjgkWOnuAVbkNpwVxRC3CaEOC2EiBRCvFrO+ZZCiD1CiAIhxIulzi0SQiQKIY6X026yqd8TQohPTGUPCiEOFzsMQoiOVRTVLChbCQ0QIQSvvfEKj0x4mKNHj+Lj40OnTp3MusQvhOC1N1/lkUfHWmwMBYXqkp2ZhZOnfYkyW7UN2vzCaveZlpqKE3Zlyh0MtqSnp1e7X3MhpWTNqjUs/XY5eTm5tOvcjhdee96sXkJXxli+cDm5RWO8QGBgoNnGKI3GToN3TYwPz1R8WgihxujuNwSIBg4IITZIKYvHTEwFpmBMaVyaJcAc4PtS/Q7AGGegvZSyQAjhCyClXA4sN9VpB6yXUh6+zndVIxTFoAETGBho0S9sbY2hoHC93NS7BxE7LtHWJ7yo7EJGHM3btap2n9179GC5fiFdZeuiaKIGaeCiPpHOnTvXWOaa8tWc+Wz6dgvt3btiZ29H7L5oxt07gVUbV+Dh4VF5B1Vg/pz5/LZoC108u2LnZEf0gWjG3zueFb+sMEv/5aEr0Fna+LAbECmlPA8ghFiB8YZepBhIKROBRCHE7aUbSyl3CCHCyun3KeAjKWVBsT5Kcz/wY43fwXWiPL4pKCg0OJ569hkOaWLYkxBBbFYy+xNO8bf2LM9PqzDpXIU0atSI3qMGsT7mHy6kx3Au7TI/Rf/NyHH34OPjY0bpr5+CggJWf7+Grl49sdfYI4QgyDUE37wgVv64ymxjrFm6hpt9ro4R4hZCYEEQq1esNssY16KGWwneQoh/ix1PlOo+CLhc7HW0qaymNAf6CCH2CSH+FkLcVE6dMVhBMVBWDBTMSkREBCuXrSI3O5fbRtzKgAEDGtT2QWJiIj/9uIKLZ8/RoVtXRoy+CycnJ2uLVSOklOzdu5df1mxACBV33X8XXbp0scgYv65dj0qlZviY0RZ9yvb29mbZ+lWs+2kdJ/87RvPWfXn13rur9OSs1+vZtnUrf/+6FRcPN0Y/cF9RMqYXX3uZA4MHsHHNBjQaNW+PeYIOHTpY7H1UlaSkJJyEc5nop952vpw8etJ8Y6jKjuFr70vEUctmKqqhV0JyJbkSyuvcHGaLGsAD6AHcBKwSQjSWJottIUR3IFdKWcY2wdIoioGC2Vjxw0oWzlhIc9uW2KrtmL1zDpt7/8qnX8xoEImazpw5w0sTnqS/gy9dnd059d06xi1fwTcrl+Pu7m5t8arNJx98zO51O2nl0ByDNPC/7W8ydOztTJo6yWxjfPrBR0Rs+ote7mEYpIHP/n6F3g+M4Kkpz5htjNK4uLgwdtxYGFf1NgaDgecmTkKejKGLWzBZham89uuTjJ/2HHcMH44Qgm7dutGtWzeLyV0dfHx8yCEbgzSUuHEnFyTSr+PN5hvDUHaMhPxEenXoaZYxykNjZ1MzG4OzldaIBor7mwYDsdUfsES/a02KwH4hhAHwBpJM5+/DCqsFoCgGCmYiJyeHBTMXMNBrCBqV8WPl5+zHzp07+O+//+rEHqul+eyd93nIpzkhrsanzhBXD5xiz/PdNwuZ+tILVpauely8eJG/f97Obb6DipS7INcA1i9dxz3334Ovr69ZxjiwaRsTw3oWjdHEw585P/zE6PvuNcsY5mLnzp1oT0Vzb+jVz3MzD3/mfjyTW267DVtbWytKd23s7OwY88i9bPhmM+1du2KvsSc6M4okx1juve8es41xz9h7+HXhr3T2MI4RlRFFvH0s99x3N0889bhZximNtkBLomVtDA4AzYQQ4UAMxht29dM5XuVnYCDwlxCiOWALJAMIIVTAPUBfM4xz3dTbNd6cnBzFd7gWiYiIwEt4FykFVwhUBbHzr521Joder2fPnj1s3ryZuLi4WhsXIO78xSKl4Ao3+TVi9x/ba1UOc3LgwAECpV+JFR+VUBGIH4cOHTLPGPv308bGq9QYgla2nmYbw1zs/vNv2juWfDq109gQpHEhMjLSSlJVjSeffoLH3niEU46H2FWwDc/ejixZtcisq1lPTnqSR6Y9wmHbQ/yZuw3nno4sWmneMcrDknEMpJQ64BlgC3ASWCWlPCGEmCiEmAgghPAXQkRjTHP8hhAiWgjhajr3I7AHaGEqf9TU9SKgscmNcQXwiLwa+KUvEH3F4LG2qbcrBjGX4rnjlpEsXv6NEpa0FvDw8CBP5pYpzzXk4O3rXU4L8xMTE8PU8Y8TVKDGHRsWF6bR5647mPzi87WylSFsNGj1emzU6qKylLwcvHxq5/1bAk9PT/JVZV348lUFZrNk9/TyIhNtmfIMWYinp6dZxjAX3n6+JGnL7smn6/Lq/HaREIK77r6Lu+6+64Yeozws/QgopdwMbC5VNr/Y/+MxbjGU1/b+a5QXAg9d49xfGG0PrEK9XTGwUznikBzKS8+WiUWhYAGaNm2Kc7Az0ZlXjXezCjKJs4nh9jvLePBYhDefe4kRdsHcE9qeIaGtmNzkZv5b9xv79++vlfFvv2c0m6JPFUV71Or1rI05yf2PT6iV8S1Bnz59SHFIIzk3pagsPjuBXNcCbrqpPCPq6o1xTpVDbHZaUdmljCQSHAx07VqRTVjtM3z0KHbnxZJRcFUJPpochUeTEMUt10rUZLVAiXxYPvV2xQDA0ymAk6f/JCcn54a3DL8RmPvtHF6a8hJ/ntmKjbBB5api5pczcXNzs/jYqampZEcnEN7kql+6Sgj6eYbxy8rVdO/evcL2UkoOHjzI6ZOnCG/SmB49ely3N8Vjk57i88xMPt70G962DiTq8nloypP06WuVbcIaU1hYSHp6OrMXzuH1514jP+koBilxC3LnqznzzeZtYmtry+eL5vPGsy8io05jkBKHAC++WLDgmmOcPHmS/w4ewtffj759+9ba3r6vry//mz2D6a+9hVO+JEdfSFDrptw5eiTLly6jbft2tG/f3qrGtpmZmWz/80/y8wvo07dPhQqLlJKUlBQcHBxu2N9IG3sbfJvVwPjQKov1dRsh66nK5OMcJEd2eJqTedv5bcfPSja/WiQ1NZWCggL8/f1r7QcyJSWFScPH8EzjkgrA+fRETjd144OZn16zbX5+PlMefQLV5RTCVc7EGHJJcbfhq2VLcHV1vW5ZcnNzSU1NxdfXt84ao1WEwWDg8xmzWL96E7Y4YLAp4JkXnqJP394IISzqk5+YmFjhGAaDgWkvvEzUvmM013iSRgEXNbnMXbrQrBH8KuNKBsW8vDxee+ZZgnLBX2VPpC4b++YhfD5/LjY2NrUmzxX27d3L28++Riu1L7aoidDGc/eTDzP20fFl6h4+fJh3X34LsgopMGhp0aUt737ygcV+K4UQBytxC6wW/nb+8uHgclfkq8Sn5z+ziFw3MvV6xSA1J4FGzYMUpaCWsca+sJeXF3b+nkRlphDq6gUYf7x3pEYx/u63Kmy7eMG3BMXkMTC0U1HZgYQLfPHRDN6c/t51y+Lo6HhDJ4pa8NU3/L5sNx3cBiOECp1By6x35hO4IICbbzaPa9u1qMwD4dfNm0nfd5pHGl2V41JGEu+89DoLfvjOorIVRwhBYGAgr0x+lkF40baR8am8N7Ax8gSrfviRBx8ZW2vyAGi1Wt59YRoP+fXE2dYBgG6yJd99vYz+QwYRGhpaVDcpKYlXJz7PSM8euAW4AHD8xDlenvIC8xZ9XatymwND/Xy+tRr11sagwJBLlsc5Zsz60NqiKNQS7838lNVZF1gXdZw/L51kzvm9tBjaj549K/ah/nPjZnoHNClR1sU3jP07as+boi6xctlPNHXthDD5omtUNjS278SCOQutLBn89tMGenqXvFaN3HxIOH+Z3Nyyxq+WRErJ8QMHaeMdUKK8f0ATtqzbUKuyABw9epQg4VakFACohYoO9kFs/XVLibo//7SOdppQ3OxdisraejUhLuIS8fEWdf0zO7KGh0JZ6u2KQWCoH5u3rkejqdlbNBgM/PjDCn78fhX5+QUMvnUgk6ZMxMXFpfLGCrVKaGgoq7ZsZPfu3aSmpjKuS5cST0nXQqVSo5cl7ZoNUhbtb8fGxjL/81mcOPQf7l6ePDTxCQYMGmRW2WNjY/nqs1kcP3QYdy9Pxj71BAMGDTTrGFVFW6BD7VTye+Nk68rFhJpFyEtPT+er2fPY9ec/ODo5Mmbc/dx19+jr2m5SazToynFDlkjrRNgsR3adwYC6mGdKbaFWqzGIsnOjNxjQ2JS8nokx8bjblF1JdVE7kJqair9/DfbsrYCyYmBe6q1i4OzsXGOlAOCdt95jx8//EerUDo3ahr9/PM6eXeP4acNKs/SvYF5sbGzo16/fdbUZds9d/PHdeu4IbVtUtis+kj63DiEpKYlnHniIka5+jApvS0peLkvfeo/01DRG3XO3WWROSkriqfvGcqtDKAMDupGWn8PCadNJS0nlrnvNM8b1ENwogMzoVFztr24JxWSep9+w3tXuMz8/n/FjHiE804cR3n3J1xWw5uNlXDp3iedfrXrwpxH3383302Zwn3O3IoXiVEo0YW1bYG9vX0lr8yKEoFu/3hw4dJFu/mGAcRVha9xZ7pxa+54o7dq1I1GdR2peJp4ORtuYQr2WI4UxTB42tETdngN6893WL2nscdUuQ6vXkWjIoEmTkisydR0bOxv8a2J8eNFsotQb6q3xYdeuXeW///5boz6Sk5MZMWQMrRwGlHiqOZ95iFc+e5JbbrmlpmIq1AF0Oh3Tnn+J6IPHCbdxIVqXg2PTYD77ag7fzp2H+o/d9AxqVFS/QKfjo4vH+enPbWZ5Sp094zMKthziJr/GxcbQMjfuAD//tbXWn4QjIiJ4cuxkvAub4GbnRXJBDIUeyaxYt6za9iM//7SOjTNWcbNvx6IyKSWrE7ayetu6Kht5SimZ+dEMdq7/jaa2nqTKfHI97Phy8QK8vLyqJVtNyMrK4tnHJ6KKTcVf7UBkYQYt+/bgzQ/es8oKRkREBK9MfJYgvTO2Qk2kNpmnpz3PHcPvLFHPYDAwcdzjyDMZtHELJ6cwl31Zpxn/6tOMslAMAksZH/rZ+cv7AqpvfDj7kmJ8WBrlkbcCLly4gCOeZZY6HfHk2OHjFlMMdDodW7du45/tOwkODeLue0fXqbCwdZm4uDjWrfqJ5IREeg7ow4CBAytd1tVoNHw8eyYXLlwgMjKSRo0aFSXFOX3kGHd5lrSQt9NocDYYbwqlXTHT09PZ8NNaLp2NpF23rtw2bFilT7IRh49xi6svmRkZ5GRnY2Nrg7uHB44GVbljWJrWrVuzcsNSli5ezrnIC4zo0Y8x999TLQ+NK5w4coIA25KBnoQQ+GjcuXz5Mm3atOHIkSNsXPsLGhsNd941nNatW5fpRwjB86+9zAPjx3Ls2DF8fX2t6h7o4uLCjHlfsmDuXM6dOsng/kN5aOxYqyUOa926NWv/2MSBAwfIz8+nW7du5Rpfq1Qq5i36mk0bN/Hnpq24e3rw4dgvaNOmjRWkrhkSywc4amgoikEFhISEkE96mfI8kUHzVs0sMmZhYSFjH5hAwulc3NQBHNRFsXzxKuYtmlUnsrTVZQ7s38//Jr/CTXahuNs5s/qveaxe+iNzFn5dJdex8PBwwsPDS5a1bMH5Pf/h6XDVy0Cr15MlDWV+cC9cuMDz4ybQ196VVk6uHDt4lFWLlzB/+bIKb6qNmjVm76odtHXwxU5lQ2F2IZFJKSSJLKt51AQGBvLKtJfM1l+z1s348/eThBULDielJEWXSWBgILM/+4JtP/5GK7vGSCl5ad2z3P3U/Yx/rKybHYC/v3+d2Ac/f/48L00Yzy3uDtzl7MTRn1fx2MZfmLdsudXskGxsbCo1uAWjQjxi5AhGjBxRC1JZlnq68G01FMWgAvz9/bmpT0eObj9KqHNrVEJNQnYUGu9cbr31VouMuW7tOhJPFRDudsV1zp/cQn+mvfQ/ftmyrkFkKawOUkqmv/4O9/vdjKud8Sbe2D2ALZGH2PLbb9xx552V9FA+949/hEm//oaXvSPh7p5kFxaw4uJpRo9/pMxKxGdvv8sjPqE09jAuaTf38sH18nmWLVrM089OveYYbj7erEo4S2iwO8EOjuTqJH8knyffU2UVIzZLcPudd/D9/CV4p18mzC0YnUHHvqSj3DykF9nZ2fz640aG+w0qysoXLkP5cf5Sho8abpUtgqry+Ttv83QjP5p4ugPQyscL9/NRLF+8iIlTrn3NFcyHjZ0N/s1roCRGmU+W+kK9dVc0Fx9/Np3hj/bhgmo3Z/TbaTnIk2Wrlpg9cI1er2f//v18t3A5nrYhJc452rqQnpxDZmamWcesTyQmJmKbZyhSCq7QzrURf23eVu1+AwMDmbFkIX+6qHnnzCHmJF5g4OQneWj8uDJ1o89GFikFV+gZEMrO37dWOMbRvQe5p90t/J4dzecXd/JN3H80DWqJj4NHvbnmTk5OfLtiMQUd7FiTso2N2TvpOX4Q0959k7179xKKP0IKsrOzycnOQSAIVvlTUzshSyKlJPZcZJFScIW+IYHs2lbxNVcwH9oCLbFn4qt9KJRFWTGoBBsbG6Y8+wxTnrVcXvgzZ84w6dEpaLKdOBt9AacCA01CWhQlZZFSokdb61bXNxIODg7k6grKlGcV5uLuFVBOi6rTtGlTvlxcuQ+/VKvQGQxoiu0vpxfk4+rhXmE7d08PHOILGN/p6qqGlJLD0X/Uq2vu7+/PjC8/K1Pu5uZGen4mZ0+fxQYNEtALHemOGTWya7A0Qgikquw1T8vPx83dPAmmFKqAkvPA7CgrBlbGYDAw+fFnCSvsRGvX7vQMHUym4TKxMXEUao0Z52KzI+ndvwd2dnZWlrbu4urqSrPObTmSdDXweYGukF3Zkdw71hyp0ytnyMjhbLoUWZRESW8wsCbqLPdOKH+f/Ar3T3iYHekn0Rp0RWUHk8/Qtd/NN2RI5eulY8eO7L18EGkAJ40TzhontHoDBy8fqfN2NQOHj2T9uUslrvnyyCjuHn/jJs660ZAoSZTMjbJiYGUiIiIQ2Xa4OhufMHycA2gX2pEDF/8hLyEKFy8HOtzcknferzisrwK8O2M6rz/7Et+d+BtXtSNJMptJbz1f5GFQHvn5+QBmeTKfOHUqH6ak8sGOnQTYOXApP5eRjzzMwEqCIXXo0IGxLz/FN5/PxVfjSro2hyadW/O/N14lOzsbJyenem1bsm/fPjoEd+bvpH9xyrHHgIF8oaN9aEcOHDhw3XEpzIWUkpycHOzt7a8Zs2Ti1Kl8lJrC67v+IcjBngu5eYx8ZDz9BwyoZWkbNopXgnlRFAMro9PpEKUWbpr5tEEj1LQdFcLU56bUaeOruoSzszOzv/2KpKQk0tPTCQsLu6Y3QlxcHO++Mo2YMxcAaNSmOf/7+AO8vb3LrV8VNBoNb07/gPT0dBITEwkODq5yzoRR94zm9hF3cvHiRVxcXFj09SJGDR6JLTY4eDry+vvT6lwKYnOh1Wpxt3Olf+cHSctLQwiBu707B5MPoTWtmtU2u/75hznTP0CTl0u2XjJwxAiefu65MsagNjY2vDn9w6JrHhISgoODwzV6VbAENvY1DHAUbT5Z6guKYmBl2rZtS75tJvnaXOxtjDcRKQ2k2ETzyPhpilJQDXx8fCrMAKjT6XjmkccZogrirnBjSuTT0bE8M+5xftjwU4190N3d3YvsQ64HW1tbmjdvztuv/4+obecY4XUrKqEiqyCb1556hUVrlxASElJ5RzcYffv2ZZ5qLu1kWzwdjQGUCvWFxIsEevToUevynDlzhi9fe5WXWjfH3d4eg5Ss3LaFudLAlJdeLrdNda+5Qs3R5msVI0Izo9gYWBmNRsOHM9/jaMEOzqYd5VzKCf7N/IP7Hxt9w4UmvVHYvXs3gXkamnhcfcpo4RmIW4aOQ4cOWVEyY8rmXVt30smrfZHrnoudM63Vzfjx+x+tKpul8Pb25qlXnmZL8laOJh7jSOJRfk/dxkvvvGyVOA4rFy9iTJA/7qbtJZUQjGnWhO0bNqDT6SpprVDbKEmUzI+yYlAH6NGjB5u2/8y2bdvIycmlf/9+9fLJsK6QmJCAJ2UNOT2xIzEx0QoSXSU9PR0ntWMZmwIPe3diLl3fmqeUkg0/b2DVdz+Sn5fHkOG38ciEcXVyqXv0PaPp068Pf2z7A41aw8DBA622WpYQHcOtpYITqYTARa0mNze3jKfE0aNH+X7ebGKjLtG6Y2fGPT2Z4OBgFGoPJYmSeVFWDOoIrq6u3HXXXTz88EOKUmBhOnTsyFl9eokyKSVntWm0b9/eOkKZ8Pf3J19TQKG+sET5pdzL9Oh383X1NeODj1k1fRG9Cpoz1OYmTizdxeMPPYqhnOyEdQFfX1/uf+B+7hlzj1W30Dr16sWBhJIKYkZ+AYX29mWiGe76ZwefTnmMezRxzOrqRZf4Qzw/dgwxMTG1KXKDR/FKMC/KioFCg6NZs2Y06dmJn3YfpI93U/TSwI7ks3S5rb/Vn/RUKhVTX32WL/43k44ObXGzcyUy8zzZvvmMvGtklftJSUnhrw3bGBMwuGj14Sa/dvwRs59/duygX//+lnkD9YB7HniAJzdsQFy4SFc/P+Kys1kRHccz0z8ss5Kz4LOPmXZTMF6OxhWom0K8Mcgkvv96Hq+9+4E1xG9w2NjZENiiBsaHceaTpb6gKAYKFsNgMLBz5072/rOHgOBAevftzY7tf5MYl0ivAb3p0aOH1ZLNvPPJdH779Tc2r16HSqXivinPM2jwYKvIUpqhdwwjJCyUpd98z5mEKPrcP4D7HrjvurYAIiMjCdCUTQAWpPHm2H9HFcWgAlxdXfl6xQpWLV/Okp078WvUhLff+YAWLVqUqCelJC8tGS/HxiXKOwV5suLwwdoUuUFTWKAl+rRifGhOFMVAwSJotVqenjCR3DPphNr4czBzF29Nncag8N54Orjzxfo9/NA+gC++/tIq+QBUKhXDbh/GsNuH1frYVaFt27Z8/MUn1W4fFBREir5sOOUUfSY9m4aX00KhOK6urjz21FPw1FPXrCOEQGXvSE6hDifbqz+l51KyCA5rfM12CmZGgkHZEzArio2BgkX45ecNaE9nMcCvO409QrmQcInRnrfgledKE88wBvreTNqRRH7f8ru1Ra2XBAcHE9qhKQeTTmCQBqSUXEi/TKxDBkMslC68IXLf40/xxaHLZBcY4y3EZ+Xx9ckUHp5ouRDqCmVRvBLMi7JiYEKn03H06FGEELRv3/6GymoXFRVFTEwMzZo1q1GAHnPy+y9baO3WFICswhxspQ0etq6ka7Mw6PWo1GpaOIWzdeMWhg4bamVpjSQmJnLu3DlCQ0MJCgqytjg1ZsaXn/HFjJn8uGkr0iBp3akt376zWAmtbUZG3n0vQgheX/g1+rwcnL39mPrJHFq3bm1t0RoMNvYagmqSXVHZhSiDohgA//77L69OeQ3nQjckkGefxYy5n9T5OO35+fk8N+l5Ig9fxEm6kiFSGDJiIK+9+arVQ+g6uzqTF5UPDmCj0lAojU9UEokw+efn6wpwdnWzppiA0RZi+lvvcPjPnYTYuhFXmEVI5zZ88PknN3SuAjs7O15+41VefuNVa4tSrxkx+h5GjL7H2mI0WArzdVxWbAzMSoPfSsjNzeWlp1+hs7o3ndx60NmtBx3EzTw/8UUKCspm66tLfPbx56QcyqOb60DauHXlZpdb+GfNfjb8vMHaonH/+Af5N+sEOoMeBxt73JxcOZ11AQcnR4RKoDPoOJx3ijEP32dtUVn14woS/jrEU416cmdgW54IuxmbYzF8/eVca4umoKBQKbJG/xTK0uAVg507d+JZ6IuTrVNRmbOtM24FXuzZs8fi4+v1+mq33brpD5q6tSp6LYSgpUtHfliywhyi1YguXbpw39SHWZu0jb+S92PjZc9h+7MctjvDjpT9rE/5g/EvP06bNm2sLSo//7CKWwJal1hl6effnG0bNltRKgVzIaWs0fdMoe5jkNU/qoIQ4jYhxGkhRKQQoswSnDAy23T+qBCic7FzU4UQx4UQJ4QQz5bT9kUhhBRCeJcqDxVCZAshXrzuCakhDX4rIT8/H7Usa0+gMqiKMu9Zgi1btjLjw8/JyszF3tGWp595nDH33XtdfUiDBEpuGdiobcjLs5zc18ODYx9i1N13cfbsWby9vQkKCuLy5cukpaXRrFmzOhOBT1tQgK1zya+CRqXCoNxMbmiklKxctpQ1ixeh1mlRu7jy1Muv0Kdff2uLpmBmLPncL4RQA3OBIRhTLh0QQmyQUkYUqzYUaGY6ugNfAd2FEG2Bx4FuQCHwmxBik5TyrKnvEFO/UeUMPRP41TLvqmIavGJw880387n6C1oa2qFWGRUEnUFHsireYglc9u7dyxsvfkCQcyc8nOzQ6bV8/sE32NnZMnLUyCr3065zW+IPRRPgcjVS4rmMUwwdc5sFpK4ejo6OJWw1QkJC6lxkx363Dmb/5n/pFdCsqOxYcjQdutfPbIYNhVU/LOff7xbyQZsm2Gk0pOfn8+kbr+M2d77VI1wqmA8bexuCW/hVv4PKo6B3AyKllOcBhBArgBFAccVgBPC9lFICe4UQ7kKIAKAVsFdKmWtq+zcwCrjiizwTeBlYX3xAIcRI4DyQU+33VQMavGLg4+PDE889yjefLyKIRkgpiRGXmPz6JItlS5s9cx6+9q2x0RitwzVqGwKc2zJvzrfXpRi8+d40HhkzgbT0JJykG+kiCddm9ox/9BGLyF1fefTpiTy5exxJl48QZutGjC6LKAcd81//1NqiKdSANYsW8W4ro1IA4G5vz/jGwSz9ai4zvvraytIpmIvCfC1Rp2pkfOgthPi32OsFUsoFxV4HAZeLvY7GuCpAJXWCgOPAB0IILyAPGAb8CyCEGA7ESCmPFN/GFEI4Aa9gXEmo9W0EUBQDAB54+AH69O/D77/+DgKG3v4+gYGBFhsvLjYeL7tOJcpsNHZkZ16fcujn58f6LWv5/fffuXjuEm07tCE5MZlHH3wclUow+v67GD5yuNWiC94oODk58d2aFez4+29OHT/BLc2aMWDQwCKPhLS0NJYtWsShnTvxCQjgwSefrPMeK/UJKSV/bNvGmu9+ID8vj0F3DmXMA/dX6jEiCwtwsCn5Exfi5kLsucvXaKFwo1LDrYRkKWVFy4PluXiVHrLcOlLKk0KIj4GtQDZwBNAJIRyBaUB5QUXeAWZKKbOt5V2mKAYmQkJCePSJR2tlrI6d2nN0RzyeLgFFZTn5GYSGXb/vvJ2dHXfeeScAzz3zPBd2RtPMpTVSGlj47lL27znA9BlKzPbKUKvVDBg4kAEDB5Yoz8jIYOKYMQyys2Oyvz8JaWnMmDSJcW+9xWAlUFCtMOezWRxc8zuDfFphr/Zl37e/sGPrn3y9dHGFSq+zlzdJObn4ODkWlR2OS6T9Td1qQ2yFWsTCgQ+jgeL7n8FAbFXrSCkXAgsBhBDTTXWbAOHAldWCYOCQEKIbxtWIu4UQnwDugEEIkS+lnGPet3VtFMXACjz34hTu3/sIyZl63J38yMpLIZPzfPRm9a/76dOnidhzhpu9+heVdbbtwe4//+TixYuEhYXVXPAGyOoffqCvrS39Q0MBcPb05HlnZ6Z//DEDBw9WVmMsTGpqKlvXbGBiaF9UpvgXg4Pasfbiv+zdu5eePXtes+2k16fx2QvPMjY0gHAPNw7HJ7I2NZsvJ06sLfEVagEbexuCW9YgwNHOSmscAJoJIcKBGOA+4IFSdTYAz5jsD7oDGVLKOAAhhK+UMlEIEQrcBdwspUwDfK80FkJcBLpKKZOBPsXK3waya1MpACu4KwohQoQQ24UQJ03uG1NN5Z5CiK1CiLOmvx7F2rxmcgM5LYS4tbZlNjehoaGs/nk5ve9oit79Iu37+fLjmsU1Mog6duwY7tqSqWolEm26nk8+/ITffv2NwsLCa7S2DqdOneLrefP5YdlyUlNTrSrLuXPneG3Ks9w9+Baee/xJjh8/DsCRvXvp5ONToq6zrS1Oej1ZWVnWELUMUVFRLFywkEXfLiY2tvSDzI3NmTNnCLP1KFIKwJiHwz1TMnX84zzxwFh27Sz/l/2mbt1459vF7PYN4ZPYVGI69mDOjyvx96/BTaQcDh48yORxE7h78C28+cJLREWVZ2BeOxgMBn5avZoHh9/N3bfcwdwvZpObm2s1eWoFU66E6h6Vdi+lDngG2AKcBFZJKU8IISYKIa5omZsxGgtGAt8ATxfr4ichRATwCzDJpBTUaYSs5eQTJkvNACnlISGEC3AQGAmMA1KllB+Z/EQ9pJSvCCFaAz9itAwNBLYBzaWUFfqSde3aVf77778VValX7N69m48nz6STu9EmplBfyK8n12Kj1dAysBX59nnkueeweMViq4dNllLy8fsfsXv93zTVBJEvC4mUMbw3ezrdLeQJUhGRkZG8NHY89/mF0dzTm0sZaSyPOcerX85ky4YNND5+nM4BV7d99AYDrx8/zqo//8TGxqbW5S3Osu+WsXDWEvxlIySSOHGJZ994hlGjR1lVLnNx6dIlXr3/CR4JMa4M6HQ6Lp47z39pF2kaFkBzrwB+ijnB/a9N5c6RI2pdvn/++pu5r73BQyHNCHJxIyI5gTUpMcz+cZlVvG+m/+9dLm89wOCA1tipbdiXEMklb8mS1T9afXVLCHGwkr38auGu8ZN93KsfKG1jymyLyHUjU+ufFCllnJTykOn/WRg1sCCM7h7fmap9h1FZwFS+QkpZIKW8gFEjUzYJS9GjRw/w1hKdeQmAw9H78dH70tG9E60D29DFsyvBWaHM+GCGlSU1rm7sW/8PwwP608a3OV382nKHZx/efuktqwSi+frzWdzvH0YLLx+EEIS5e/JYWEu++uRT7h8/np8SE0kyPXVp9Xp+iIxkyF13WV0pSExM5JtZi+npNoRmnq1o7tmaXm5DmDX9SzIyMqwqm7lo1KgRHs1D2J9wFiklqSkppOdncV6XQrfAxvg5uTKhcVcWzvoSg8FQ6/LN//QzJjZpS7CrO0II2vj4M8IjgCXz5te6LImJiRzY8hcjQzvjYuuArVpDn8CWOCfm888//9S6PLWJkkTJvFhVhRRChAGdgH2A35U9GdPfK/sv13IDUSiGSqVi4fJvcegs2J6+maNJB2ni1ZhG4Y2KzGUbuYbx727rr6Js2fgbLewalYg06GTriIfOidOnT9e6POdPn6aZR8lVFD8nF9LiEwgPD+fVWbNYkJbGtCNHmBYRQejIkUycOrXW5SzNP//8g48+sCj+BoBGpcFLH8C+ffusKJl5+XTebPQ9GjH70p98dupX9hdGMbn7EOw0RsXMXmODvQ6ys7NrVS4pJbmpabjblwzU1cbHj4gjR2pVFjBuhzWy8yiTJyXcxoMTR47Wujy1hQQMyGofCmWxmvGhEMIZ+Al4VkqZWYFbRlVcRa70+QTwBBj38Rsa3t7efPn1bAwGA6OHjca/0B+N+uolNkgDao31s0Y6ODpQaNCVKS+UOuzt7WtdnoDgYGKyMgh2dS8qS8/Pw8HNFYAuXbvy3c8/o9frUalUVk9QdQUHBwcMqrIrLAaVdebRUjg6OvLORx9gMBj4+J33cN17Dl8n16LzWr2eXPQ4OTlV0Iv5EUKgcXQgV1uIo81V18kL6amENW1Sq7KAMdV2vLas3UucLoshTZvWujy1ha29hpCaGB/uMp8s9QWrrBgIIWwwKgXLpZRrTcUJJvuDK3YIV+JRVcVVBAAp5QIpZVcpZVefUgZjDQmVSsWYh8dwJP0wxW1IItJOcNtw60dFHH7XCCK05ynUa4vK4rOTEF62hIeH17o8E6ZOZunls6TkGeNIZBTks/h8BOMmTypRT61W1xmlAKBfv36k2iaQp71qXJZdmEWWXarFonZaE5VKxUOPTeD3zChis4z2W3m6QlZdOsKosQ9aJVX6g09NZPG5E2QXGhOuJeZksyruIo88/VStyxISEkJg++bsiD2J3mBASsmJ5Mtcsstn0OBBtS5PbVGYr+XSqbhqHwplsYbxocBoQ5AqpXy2WPkMIKWY8aGnlPJlIUQb4AeuGh/+ATRTjA8rxmAw8O6b77J7yx48VJ5kyHSad2nOp7Nn1IlUwpt+2cjs92fir/KiQBZS6CaZvXAuwcHBVpFnz65dfP3p52QmJePg5sq4Kc8w5Na67wBz4MABXp3yOs5aDwwYyLfP4vOvPqVdu3bWFs1inDx5kpnvfUj8pShsHBy4Z9xDjHnwAaspbRvXr2fZV19TmJ2DR4AfT7/yMl26WseWrbCwkDmfz+LPjVuQBgPtburE89NexdfXt/LGFsZSxoeuGl/Zw21MtdtvTZ2jGB+WwhqKQW/gH+AYcMVa6HWMdgargFCMCSXukVKmmtpMAyYAOoxbD5UmlmjoigHAPzv+4aO3PyIhPhE3Dzeef+15br9jmLXFKiIvL4+jR4/i7OxM69at69TTeF0iOjqa99/6gNPHzqDSqLh91DAmP/dMkfGjVqvl6NGjqFQq2rdvb5UnZwWFyrCkYtDd7foS0BVnW+pcRTEoRa0rBrVFQ1cMDh48yCuPvUoPt1442zqTr81jT9punps+lduGWX87QaFqZGdnM3ro3TTVtibAJRC9QU9E2jHC+gfz8ecfWVs8BYUqY0nF4Ca3e6rd/s/UeYpiUAol8mE95auZX9HZqSvOts4A2Ns40N3jZr7+4mtFMbiB2PDzBjxzfAnwMubuUKvUtPPqyF87ficlJQUvL69KelBQqN/Y2tvQqCbGh7vNJ0t9QVEM6inRl2No4tCiRJmjjSNZ6bXr0lVVLl26xNJvFhF17gLtb+rCA+MextPT09piWZ1zZ87jblN2HlzVbsTFxSmKQSVcvHiRpd8u5lLkBTp068KDyueq3lGQr+WCYkRoVpRA7/WUVm1aEp9d8suSkZ+Ob5D1jZBKc/ToUSaPGYfrvsvcVuhH1sYDjL/rPhITK0+UXt/peFMHknQJJcoM0kC6PpVGjRpZSaobgyNHjjBpzHhsdsXRJzeIxJ8PMu6u+0lKSrK2aApmRtbgn0JZFMWgnjL5xckc0x0hJjMagzSQkJ3Avqw9PP/6c9YWrQyfvzud+/w60MYnBDc7R7r5N6GPOoDF8xdU3riec+uttyL9tZxOOYHOoCO7MJt9yTsZ9eBIXFxcrC1enWbG29MZ5d2Vlt6huNk50cWvOV0JZPH8b60tmoJZkRhE9Q+FsihbCfWUxo0bs3DVt3z1xXx2Hd1BeJtw5jz3Ja1bt7a2aGVIjo7Hr3HJbY/2PqEs2LnXShLVHWxtbflu5RIWfbOI3zdtxdXTlUmvTGTo0KHWFq3OkxKbiHdoSbfNVl6NWLlrj5UkUrAEtvY2hNXAxuAfJcBRGRTFoB4THh7OJ7M+trYYlaK2t6VApy0KcQuQkJuBf6B5s+DdqDg7OzPluSlMeW6KtUW5oVDbaSjUa7FVX/1cJedmEBAYaEWpFMxNQb6W86fqV1ZRa6NsJShYndFjH2BDzFF0BmPMqjxtIRvijzP26SesLJnCjcw9jzzA73GHr36udAX8nnyUcZMet7JkCuZGsTEwL8qKQRWJjY0lPz+fsLAwq6cvrQgpJVFRUQghCAkJqXNBg5KSkkhPTycsLKwoQM/D4x+hMD+fL5evwhE1hbaCJ//3It26KUk0FarP2AnjKMgvYNHyVdhjg95O8NQ7L9KlSxdri6ZgViSS2s+sWZ9RAhxVQmJiIlMnPkdyVDoaYYPePp8PPn+Pm266yQxSmpczZ87w8qQXUWWZEiZ52jBj7mc0btzY2qKRk5PDm88/T0LEKbzs7LhcWMCTr7zCbbdfjcSo1+vJzc3FycmpTitfCjcWVz5Xzs7OdU5RbkhYKsCRs423bOs+vNrt9yUvVgIclUJZMagAKSVPTZiEe2IIN7l0AiBXm8OLT73Kut9X1yl/6IKCAqZMeIY+tjfh6eUOQHJuCs+Mn8SGP35Bo7HupX7nlVdpHZfMY206AJCn1fLp9A8Jb9qEFi2MhodqtVqxtFcwO8rnqn5jND4MqHb7fTvNKEw9QVEMKuDs2bPkxBfSwuWqsZKjjRM+WSH8sn4jj4wfa0XpSrJz5058CzzwdHMvKvN29MIjyZl9+/bRq1cvq8mWlZXFxcNHGNe2Y1GZg40NI3wD+GnZMl5/7z2ryaagoHBjYzQ+jLG2GPUKRTGogKysLGywK1NuJ+xJT0uvfYEqID09HTtZNmuinbQlIyPDChJdJTc3FxcbmzLlbvb2ZKamWWzchIQEPp8+gyMHDqOx0TDivlFMeOIxJcmQgkK9QmJQbAzMirKRWwGtW7cmUySj02uLyqSUJBLNgMH9rSdYOfTo0YNo4jHIq18QgzQQLROsbg/h6+tLlo2G9Py8EuV7khLofestFhkzNzeXR+8bh82hPMZ4DWaEc2/2LvmD99981yLjKSgoWAcJGGrwT6EsimJQAQ4ODjz/+lT2Zf5BVPo54rKiOZS+k66D29e5fPdBQUEMe/BOfkv4iwtplziXdpHNCX9y72Nj8PHxsapsQgheev89Pos8xT+XoziVksTSyFMkB/pzq4UC9Wz6ZSPBeZ409QhFCIGN2oY+fl3Y98duUlNTLTKmgoKCdZDCUO1DoSzKVkIljBg1gnYd2rFm5VpysrJ5cvhrdO/evU5aN09+bgr9BvXnl582oFKp+OTup2nTpo21xQLgpm7dmLN6FetXryEiPp6B/cczcNAgiy3rnz15Fj/bssah3mo3YmJi6pThaH0mMjKS5UuWkxSfRL8h/Rg+cjh2dmW35xRqH51Ox6aNm/jr1624ursz5pEH6mRk1Mqws7chvGVQtdsfVlJnlEFxV1Sol/y87mc2fbySXr4di8qklKxM2MbKrT/h5uZmPeEaCH/+8ScfvjydNjYtcbVz5XzORfQhku9WfoetbVl7GIXaw2Aw8NS4J+BsOu1cw8guzGVP9hnGvzqJkaNHWWRMS7krOtp4ymYe1d+SPJq0UnFXLIWylaBQLxk6bCjJbrkcSz6LQRrI1eazLX4fg0fdqigFtYDBYGDGO58w2LM/4R5heDl6cpNPZ9SX4Zf1v1hbvAbP33/9hfZMCoMDOuPn5EkTj2DGBPbl60+/pLCw0NriXReKjYH5URQDhXqJnZ0di1d+h/dtjVmb9TfbxRFGvfQAL7z6orVFaxDEx8djW2iDvca+RHmYYyP++eMfK0lVt9Fqtfz999+sW7eOqKgoi461e/tOmjuU9P23UWvwU7sRGRlp0bHNj0Sir/ahUBbFxkCh3uLu7s5rb70Ob71ubVEaHK6uruTq85BSlrDHySzMJCAwxIqS1U0uXbrEU2Mn4p7rgoPejgWq+QwYNYiXXn/JIvZMfkH+RGrPlSnP0OfecPY3dva2NG4ZXO32JxQbgzIoioGCgoLZcXZ2pkufrpzYeZI2nq0QQpCrzSVCe5rnx75sbfHqHK9MeZmutMPX5EHUUUq2rd1B30F96dGjh9nHGzF6FGOXrKBpQRBuds4AHE89T0DLRvj731hZTQvyC4k8ddnaYtQr6u1WgsGg7B0pKNQWqampxMXFUdyY+e3pb+PT149NKVvYlrqdnfp9vD3rHcLCwqwnaB2g9FwlJiaSHZ+Jr9NVt2IhBK0dmrN+1c8WkcHHx4fpX33Gr9rjrIz7h+9i/iSrtRMfz/7MIuNZEmMKJX21j6oghLhNCHFaCBEphHi1nPNCCDHbdP6oEKKzqbyFEOJwsSNTCPGs6dwMIcQpU/11Qgh3U/kQIcRBIcQx09+BZpusKlJvVwzOnorklRde493pbyvuUQoKFiIlJYWXprxEzJkYbFQ2qFxUfPjFh7Rp0wZ7e3umf/ohubm5ZGdn4+PjUyfdfGuL5ORkXpr8EjGRsdgIGzRuaj784kN8fHzKTf4rkagsOF+dOnVizZb1JCcn4+DggLOzs8XGsjSWzK4ohFADc4EhQDRwQAixQUoZUazaUKCZ6egOfAV0l1KeBjoW6ycGWGdqsxV4TUqpE0J8DLwGvAIkA3dKKWOFEG2BLUD1/TGrQb1dMXBUu3Lqj1je+98H1hZFQaFeIqVk0qOTcL3gwi1eQxjg0Z+Oug5MfXQqmZmZRfUcHR3x9fVt0EqBlJJnHn0Gl4uuDPYcQj+P/rQrbM+UCVOws7PDLcCN+OzEovoGaSAi7wwjx1jGdfAKQgh8fHxuaKXgyppBdY8q0A2IlFKel1IWAiuAEaXqjAC+l0b2Au5CiNKZnQYB56SUlwCklL9LKXWmc3uBYFP5f1LKWFP5CcBeCFGrT7f1dsUAoLFra/7euoWCdwqstmqQmppKfHw8YWFhODo6WkWG4kRFRZGfn0/Tpk2V1MYKVUZKyfnz5xFCEB4ejhCCs2fPkhebS4jnVWNCN3s3ArMD+XXTr4y5f4zF5CkoKOD8+fN4eXnh6+trsXGqSmXf87Nnz5Ibl0uwx9W5crV3IyAnkN9+/Y2Pv5zB049M5FzyRewNtsSSyND7htGtW7dqydOQvud29rY0bVl9g9azlRsfBgHFjRiiMa4KVFYnCIgrVnYf8OM1xpgArCynfDTwn5SyoFIpzUi9VgyEENgIO/Ly8mpdMdBqtbz9+tsc+OsArmoX0vRp3PPIvTw56UmrPDnFxcXx6qQpiOR0HNUa4tHxyofv0d0Chk01JS8vj2/mzGP75t8RQjB4xDAee2qiRYPiHD9+nNkfzyLm4mV8A/yY+PzTRXMTFRXFzI9nEXHkBG7ubox/ajxDb7dMKOe6yMmTJ3lt8os456kxSAOFrio+mTeL9PR0HCh7E3QUjiQlWM7Ue9WPK/hu9gICNC6k6nJp1KElH3z+cY0Ub71ez3cLl7Bh1Tp0hVp6DerLpOcm4+rqWmE7rVbL/159kyM7D+KpcSFRl8694+/n0YmPl6iXlpZW7lw54EhyQjIhISH8vHUD+/btIy0tjbCwMFYtW8UtvW/F3sGeMWPHcP+D91V6k4+NjeWlp6eiT8zGXqUhVVPIWzPep6uV86VYkoL8Qs6culSTLryFEMWj4S2QUi4o9rq8H+zSuz8V1hFC2ALDMW4XlGwoxDRABywvVd4G+BiwTEKZCqjXikGeNgd7N41VAtrMmTWHS9svcpu38ZoapIFNCzcS3iycW2+9tVZlkVLy4pNPc6fKnSZNwgHIKMhj+vMvs3jjz3XKPUlKyeRHnyQopoAn/W9CItm59h+eP3KMOYu+sciYERERvDBuCgPdutDToxkpaem8M+kNpn3xDs2aN+PRMY/RmtYMdBlMbk4uX74xh/T0dO5/8H6LyFOXyM/P54XHJjPKrQuebsabZGJOGs9OeJrvf15BkkxEb9CjVl0NbR0rY5nYf6JF5Dl06BCrZ37L46G9sVEZf74OnIrkw/+9x3szPqx2v/979Q0SdkQy3KcbNk4ajv0eyWP7x/HD+lVoNNf+mZz92Rek777MGP/BgOl7/u06wps1ZuCgQUX12rRpQ5Kh7FzFEcukfk8BoFar6dmzJ3l5edx9+z0EZIXQx20QWl0hqz//iehLl3nljVeuKYuUkmcfe5p+hhBCg42rKFmFufxv8sss+3UtHh4e1Z6fuow0xTGoAcmVRD6MBoovSQQDsddZZyhwSEqZULyREOIR4A5gkCxmuSuECMZoizBWSlnWr9TC1Ns1Jq2+gBP5O/nf9Des8oS+ee1mOni2L3qtEiq6uHdm6YKltS7LmTNncMrIpYnHVatnNzsHejl6s3lD3YpCd/jwYYhKoW9QS2zUamzVGgYGtSbz9GVOnTplkTHnffYl/V064efkDYCXgzu3evVgzidf8P2i7wnThRHkGowQAidbJ3p792HhnEUNwvNlx44dNJaeeDpcfXL2dfLAX+tEREQEj059jG3JfxCVHkV8dgI7k3bRvFdzOnToYBF5Vi5eygCPlkVKAUBXnyYc+mdPtSP2xcfHc3THQfoHdMFOY4tKqOjg0xzXVDV/bd9eYdstP2+mm0/botcqoaKXZ0eWf1vye+7s7MyjUx9le/KfXM6IIj47nt1Ju2jZqwXt27cvUXfTxk04Z7gT7t4ElVBhp7Gnq1cPtvy8tYTtRmlOnjyJY4aeUNerWysuto601/jz2+Zfr2dKbjgsbGNwAGgmhAg3PfnfB2woVWcDMNbkndADyJBSFt9GuJ9S2whCiNswGhsOl1LmFit3BzZhNEzcdZ1TYRbq7YqBo4cdy9cvISSk8r0nKSUHDx5k3er1CKFi9JiRdOrUqUbj63UlnwwAHG0cycy49hfbUmRnZ+OisSlT7qyxISsjo9blqYjIs2cJwpHTKbEcjD2HAG4Kbkao2oXz58/TsmVLs495MfICXd0GlChzs3chIzmNiCMRBDuFljinUWmw0WvIysqq9+GVszIzsZdlfybspfH9P/DQA3To1IFVy1eRl53H5JGT6d+/f42V8ezsbNb9tI4Th47StFVzRo+5Gw8PDzLSMnAulRxLCIGt0FBYWFit7aYLFy7gp3YvU+6v9uDUiVMMHjKk3HZSSqTegEqUfL5ysnEgM73s9+rBhx+kQ6cOrP5hNbnZeUy5xlxFHInAW+NdokwIgZvKg+jo6GsmOsrKysJRVfb9O6hty5Wn/iAxSMtFMDR5DTyD0TtADSySUp4QQkw0nZ8PbAaGAZFALjD+SnshhCNGj4YnS3U9B7ADtpo+A3ullBOBZ4CmwJtCiDdNdW+RUiZSS9RbxcA/wL9KSgHAZ598zs/LtuKjaoJE8temVxnz6J1MfvaZao/frHUz4iLjCXC5GizkdOppBj84uNp9Vpc2bdpwNj+LfJ0We5OCIKXk3+wUnhs4oJLWtUuTpk35MvYUnoUaerg1xiANbDy6jwwHPQ+Gh1tkzEZNwoi/mIS/89UVlcyCbNy8PWjZriWRkedoates6JzOoEOr1t3gltxVo3uPHnynn0932aroBqg36DmnS+Im0751mzZteGf6O2YbMzk5mQn3jiU835MQRz9OHviHh5avZv4PCxkwbAj75q1lkMPVtOfJuRk4eLtV+3qEhYWRoC9744zXp9GvdYtrthNCEN6iCbGxiQS6XH1KP55yloEPlq9MtG3blrbT25Z77got27Ui4tcNBHI1mp+UkkxDGkFB1/Zaa9euHRe1qRTqtdiqr37PIwriuWdg/wrHvJGxs7ejWctG1W5/aWfldaSUmzHe/IuXzS/2fwlMukbbXMCrnPKm16j/PvB+5VJZjnqrGFSV6Oho1v3wK61c+iFMP3zeMoAVS9Zx7/334OfnV61+p703jccfeJzklCQ8bDxJ0CaQ71vA+MfGV97YzNjb2/Pkqy/yxfQZDHALxFGjYXd6Ao3796Rt24p/pGobLy8vCvIKuNO3Ey629kjAx8aFr5P24OVV5rtlFp5+YTLPj53MENVNeDt6kpafyR8p+3ll5ls0a96MhzY8jFOWE/7OAeTp8vg3bT/jnn3EYimj6xLBwcEMfXAkK5ZvoLNjGAZp4FDeJe576hGL2abMmzWH9togWvs1ASDQxQevdHc+++ATPpn9GVvWb2Jz9H80d/QjpSCbQ7pYPvl2TrXHCwgIoE3PDuzYfYgevu3QqNREpJwnzV1L/wEVK86vv/8mTz30OM2SAvCxdScqP4EMbx0fjB9bbXnuuPN2lsxfwsX084S6haEz6Dia9h8DRwyocIXK0dGRp159lm+mz6KbYzh2ag2Hcy7T/tZetGrVqtry1HUK8gs4c+qCtcWoVzT4tMtr165l7uuraexV8gZ5Mf0Yz80Yy7Bhw6otQ2ZmJuvXruf82fN0vKkjQ4cNtWq62bNnzzJ31hdkZWbx8KPj6devX53zLf/pp5/497MVtLDxJtu07eLi7spxbRIDpk2o0fWoiCNHjjD7o5nEXY7Fy9ebp16YRM9evQDjUvPnH37O6eOncXJ1YvxTExg+4k6LyFFXOXHiBJt/3ohKpeKOu4bTosW1n6Rryp39b2OMe98SS/RSSpYlbOPXPX+g0+n4848/OPDPHgJCgxkxetQ1lUYpJceOHSMmJobWrVvTqFH5T5Y6nY7F3yxk4+r16LQ6evTryeQXn8Xd3b1SeTMzM/n5p3VcirxIx+6duPW222r8PU9KSmLWJ7PYt+sA9vZ23P3Q3Ywd93CVXA8vXLjALz/9TF5OLkPuHEqnTp0s/j2PjY3l6NGj+Pr60rFjx3LltFTaZVsbZ+nv3rHa7S8n71LSLpeiwSsG27dv5/2pc2nsUtKm4Hz2Qd776kV69uxpKRFrlYiICKY89iyuBe6opIZkEc/kVycx+p7R1hatBH/++Scb/jeH2wJLGq9tiP2Phz5+ud5cD4Vr88Dwe+mnb4GLrVNRWYGukJ9z9rL+z80VtCxJZmYmkydMhLgsvHDksj6d5r068t6MD+u9b39tIaXko/c+5J9f/iJY5UOWyEXnreKrJV/j7V3GTsIyioHGWfq6t6+84jWISdmjKAalaPBbCb169cLg8iFZ+am42BuXRjPykhFuuXTvXjqGxY2JwWDguYkv0FHdHVcP41KkzqBjzvR5dL+5O8HB1c9MZm569+7NLNWHJOSk4edkdK+Kzkomzrag3lwPhYp54LGxLHtvPncE9UQlVBikgR2Jh7nriXuvq58Z731Ik2Q7OgReNdb7dfdB1q5Zw933Xl9fCuXz5x9/cGjDXkYFDCpalbiQfpn/vfImcxd+VSsy2Nvb0bxlWLXbx+zaYz5h6gkNTjFIT0/H1ta2KBiKra0tC5fOZ+rTLxCXaAx97RHgxMJ5X9ebPeTjx49jn+eIa7H9SY1KQwjhbN74K0+UCsZiTWxtbZm5aD7Tpr6IiD6FBDQ+Lnwxv/5cjxuFwsJCsrKy8PDwqNUn7NvvvJ34mFiWfbcCT7ULqbosbrnrdh6+zn37Azt2MzGkpLFvb99WrP/xJ0UxMBNrf/iJzm6tSmxVhLuHsProFvLz87G3t7e4DPn5BZw+dd7i4zQkGoxiEBERwbQX3iA7KRcdOtp3a8f7H7+Li4sLjRs35pff1hEbG4sQgoCA0iGub2yklIjyAnMJgV5vOTef6tKkSRN+/GUtcXFx9fJ61HUMBgNzZs1h/Yr1OAh7tBotk16cxPBRpcPDWwYhBI899QQPjR9LfHw8vr6+1YxqWPYzrxIqDPr6H3+ittDr9WXcNcF4DWtvm1oipXJNzUmD2GhLS0tj0rjJhGe1oZfbYPq53Ub6/jyem/R8iXqBgYH18ibUpk0bsm0yydXmFJUZpIEYeZHbhtVuFMaqIoSot9ejrrNowUJ2Ld/BHZ63MsRzAIOd+vPVu/PYt29frcphb29foxwj7bt35mRqVImyvYmnGHb3cHOIpwDcec9wjmScLlEWnRlHcLNQHBwcakUGCRhq8E+hLA1CMdjw8y/460Jxtbu6lB7u1pSoiBhiY0tHtqx/aDQaPpo9nX15OziWcoiI5KPsSPudh555kHALxQZQuHFZvXQ13by7Fj0J2qpt6eramUVzF1pZsuvj1bff4D/7JDbFHGRvTASrY/ZAG1/GPFD/Q1nXFkOHDaNR3+ZsjP+bQ3HH2ZFwgEPqs7w7ozbd8C2eXbHB0SC2EmKjY3FSuZQpdxCOJCcnExgYaAWpapeuXbuy6a9f2L59O3l5efTu3Rt/f//KGyrUG+Li4pg7cy6H9h3C29eLxyY/Tt++fcvU0xXq0KhK/jS427txNC6iTN3K2LZ1K999tZi01DS69+nBxClP4+PjU3lDM+Dp6cmKjWvZu3cvUVFRPNi2LW3atKlzLro15cyZM8z+dA5nTp0lrHEjJr8wiXbt2lXe0AyoVCo+/Owjzpw5w3+HDuEfEECvXr0qzC9hbuzt7WhRA+PDxN3/mU+YekKDcFf8+++/+fS52XTxuLnovN6gY2f2Vjbv2FhrS14KCtYiOTmZB0c+SAtdMxq5h5JZkMWB9AM8/saTjBo9qkTdh0Y/SJOUMDwdribdOZ1yhrARTXl52stVHnPZkqWs/fJH+nh3xsXWiTOpF4mwieKHDSsrzVqoUDXOnDnDY/dNpKm6E96OfqTlJXO68CCff/sxXbvWLQ88S7kr2mgcpYdrs8orXoOktKOKu2IpGsRWQp8+ffBt48GR1AOk56WRkB3L7rTtPPHs44pSoNAgWLZkGWGFoYR7hKESKtzt3ejn05evZ84vkwzq1XdeY1fOXs6lnScjP4PjySe45BjNoxMfrfJ4hYWFLP16CbcF9MbNzgWVUNHSqzHhBb6sXrHa3G+vwTLz4y9opu6Mj5M/Qgg8HX1oY9+Dz6bPsrZotYiylWBuGsRWgkqlYv6ir9j4yyZ+2/Abrm6ufDb+Yzp27Ght0RQUaoWjB48Q5lwy6p+t2haVVk12dnaJJ/i2bduyZN13LF20lAtnL9ClR3c+fej+60oYlZycjKtwAoMkKSUJrVaHi4sLwY5+HD90tNw2J06cYNO6jag1au68azjNmzev3putw1y4cIF1a9aTm5PDsOE1j0oYefocnZ0GlShztXfndMz+mop6wyBB8UowMw1CMQCwsbFh1F0jGXXXSGuLoqBQ6zRp0ZS489G42F1NNKQz6CgUhTg5OZWpHxwczGtvvVbt8Tw9PYnPSuJc4jnshD0qoSIxI4ELuhh63H5LmfrzvpjL5u9/oaVtEwwYeHb1M4yZ9CCPTHik2jLUNdavW8/n73xJIE3RqDT89fNb9B3enbfefbPyxtcgKCSQ9POpuDtczVuRU5iNu7e7GSS+UZBIC2ZXbIg0GMVAQaEhM+7xcYzdNBbXHDd8nLwp0BWwL2U/D0y83yKBo+zs7MjKz+FwViQ3u3fAVmVDekEWB1IjGB7wcIm6MTEx/LL0Z+7wG1zkCdHY0Ijl877nzpF3WixZU22Sm5vL59Nnc5PrIDQqY+bDQBnKjg1/EXFfxDVTKVfG5Bcn8dz4V2in7omzrQt52hyOZ+/lf++/ak7x6zT29na0bNmk2u137ztdeaUGRoNWDM6dO0d0dDTNmzcv4y8fGxvL2bNnCQ0NVVz66gAxMTFERkZe9/XIzs7mv//+w9nZmQ4dOlQ7gl9qairHjx/H29ubVq1aVXv5t7rvo6YEBQUxb9k8Pnn3Ew6c+xd7R3seeulh7r3PMhEAExISCHQPwcXOlfUJf2MwGHB1cKdv01vYu2Mv991/X1Hd3bt3Eyz9SwTKUavUBKv8OHDgALfeWjdjbVwPx48fx9XgXaQUgDFWh5chiL//+LvaikGXLl34aP57zPxoFhHxqbh5uvDmOy8zoI6lU7ck+fkFnDx11tpi1CsapGKQm5vLM09O5cLxWOwNruSoUug/tCdvv/8WANNefoO9fx7ERXqSIzJo1jGML76aiZ2dnZUlb3jo9Xpee+kN9v91CGeDJ3mqTJp1asQX8yq/HmvXrGXux3PxFT4UoqXQpZA5i+ZcM8NeeUgp+XLml/y8fAOe+JCvysM5yIGvFs/Dw8Oj8g6KvY9XX5rGvu3/YW/woECVQctO4cyuxc9VixYtWLi8dmIRODo6Uii1dArsQqfALkXlUemX8PAqOW9ubm4UqnRl+igQWlxcyroZ34g4Ozujo7BMuU4U4uZRdduN8ujZ82Z6bri58or1FanYGJibBuGVUJpPPvyU5KM62jr3palrR9o7DWT3L0f46ae1/Lh8BYe3nKWryyBauHais0t/Eg5m88Vns60tdoPkh2U/cmzbOTq7DKK5Wyc6uPQj9t9sZs+cU2G7ixcvMm/6PG7xGEJXz6709LyZtoVteG7ic9cVqnXXrl1s+m4LfdxuoZ17F25y7Y1zjBfTXnrjut7Hsu+X89/WSFo59SPcpT0tnfpw6UBGpe/jRsXV1ZUWHZtxPv1cUZlWr+WM9hT3PXxfibp9+/YlziaJ9PyMorKU3FTS7LPo1q1brclsSVq1aoWtDyTnJBSV5WlzSdZcZujtQ60oWX1A8UowNw0ijkFp+t40kPZ2QxDFli7ztbmkeJ9Ep9USlt0JO83V5B8Gqec/7Xb+3PO7xeW+Fnq9nsLCwgbnXjn8llGE5nTGVnP1qdog9Ryt5HrMnjmbEz8cp7lXScv2v1N3MPPHmTRpUrU9yclPTIFjDng7lgzK81f6b2ze+UuVr8ftQ0bgl90RG3Wx92HQE8k/bN9d/vu40a95ZmYmLzzzApdPXsZB5USmyGDqq1MYPrJsSOKIiAheeeYl7HI16KUB3FR8Nn8mjRs3toLkliE2NpYpTz5LRlwuGjRo7fOY/vl79Ub5qQxLxTFwdnKVbdv2qHb7ffu3KnEMStEgtxIMBknpBCtqlQatVouuUIu6VNQ3gQqDlZIN6XQ6Pv7oE9at+wUpwcfHkw+mv0uXLl0qb1wP0Gq1qFUljeMEAr2hYk2/IK+gTPQ+AI1QU1hYdkn3mv0UFOIkyi5nq4TquhJQabU6VKLU+xACvbZsH1qtlk8/+pxN639DZVDj4evG2x++QadOnao8Xl3A1dWVb77/hvj4eNLS0mjSpAm2trbl1m3dujUb/tjIuXPnUKlUhIeH17sIhYGBgaz5ZRVRUVHk5+fTtGnTWs1aWV/Jyy/g5Kkz1hajXtEgP5VdunckIbtkcpWozJPcOWoYQ4ffysXM0klBLtC9t3W0+nffeZ/VK3/F2S4UV4dGZKZqeOLxSVy+fNkq8tQ2t915CxcyTpUoi864QM8+3StpdxvntRdKbBvkFOaQa5dHixYtqjz+nXffzvm8kp+H5Nwk/Br54OzsfI1WZRl6xy1EZ5X88YrPukivfmWfdN5/Zzp/rzpMe7shtHMcjEdqCyY/+gIxMTFVHq8u4e/vb1xKv4ZScAUhBE2bNqVx48b1TikoTmhoKM2bN1eUArMhQRqqfyiUoUFuJSQnJ/PwmPHIZHvs9K7kaFLwb+nGwu8WYDAYmPDQY6Sfy8NZ70muJhPhXcD3KxfXuttUXl4efXoNwNm+UYkfyozMJEaPGcS0N16vVXmsQW5uLhMeepy088brkafJRO1TyPcrF1dq/PfZx5/x+8rfCRbBaIWWaFU0n8z75LpCxRoMBp6d9ByR+y7gZfAjT+SQ6ZTCgmVfExYWdl3vY9yDj5JyPh97nTsFmizs/XQsLfU+cnNzuaX3HbR3GFLimsdmnqPHmOa8Mu2lKo+poFCXsNRWglptK52dfKvdPjMrRtlKKEWD3Erw9vZmw29r+euvv7h4/hIdOrWna9euRRr88tVL2bdvH8ePnqBx03D69etXq0lBrpCRkYEQmjJPT7a2Dpw/f7HW5bEGjo6O/LBmKXv37uXEsYjruh4vvPICo+4Zxd9//o2Lqwu33HbLdcfoV6lUfDFvFseOHePAvgP4BfgxePBg7O3tK29c6n2s+Gl50fto0qwxffv2LfM+0tPTsVM5lrnmTrYeXLrYMFaJFBSuF8Urwbw0yBWDGwWDwUDvXv3RSF/U6qs3kLTMWJ5/6XHGjn24gtYKNyJ6vZ5BvW6lueiLjfrq0vv5jKM89ModPPjQA1aUTkGh+lhqxcDJyUW2adO52u0PHNihrBiUokGuGNwoqFQqXp/2CtNeewdbtSd2tg5k5abgH+jM3XePtrZ4ChZArVbzwuvP8vEbswhWt8XJ1oW4nItogvKUcN4KCuWQn5/PyZNK9EJzoigGdZw77rid0NAQ5s//hoT4BB4YfD9jxz6Mo6OjtUVTuE60Wi3btv3BscPHadGqGbfedmu5WxJ3Dr+D0EYhLJy/hMSEC4wY0pcHH35Aueb1DIPBwJ49e9izcy9BwYHcMfyOehPQqfZRthLMibKVoKBQC2RlZfHgmHFkR4OD9CBPZKDxymXZysX4+lbfcErhxkSr1fLEhKeIPZqKO37kySyyHBNYsHQezZo1s7Z4FsFyxoca6ejgXu322TkpylZCKRR/GQWFWmDul/PJj3KgkUsHfF1DaeTSDpuUQD56f4a1RVOwAj//vJ74w5m0dutOoFsYTdzbEa7vxOsvVj/TYkNGiXxoXm6YrQQhxG3AF4Aa+FZK+ZGVRVJQqDLbtvxJkEvJWBg+zsHs37vDShIpWJNNa38l2LHkyoCbgxfnLv9Hbm6usm10Hdjb29OqVdVjk5Tm33/3VlqnsvuPMLoRfQEMA3KBcVLKQ6ZzF4EsQA/orqxOCCE8gZVAGHARuFdKmWY69xrwqKnNFCnllmq/wWpwQygGQgg1MBcYAkQDB4QQG6SUEdaVTEGhajjY26PL12JbLIqjQeqxsTF/ymOFuo+zizNZ+nzgqvuslBI9equ4Rt/IGI0PT1qs/yref4YCzUxHd+Ar098rDJBSJpfq+lXgDynlR0KIV02vXxFCtAbuA9oAgcA2IURzKWWthd+9UbYSugGRUsrzUspCYAUwwsoyKShUmQfH3c/lnBMlIjFGZ59i1D13WlEqBWvx0IT7uVQYgaGY/3101jlu6tmp0giRCqWRIGtwVE5V7j8jgO+lkb2AuxAioJJ+RwDfmf7/HTCyWPkKKWWBlPICEGmSoda4UVTTIKB4dJdoSmpjCgp1mnvH3M2ZU2f4bcN2HHAjn0x6DOrM0888ZW3RFKxAjx49eGjS3Xy3YDkueJMvs2jUNoj3PnrH2qLdcNjb29OyVctqtz94cH9lVapy/ymvThAQB0jgdyGEBL6WUi4w1fGTUsYBSCnjhBC+xfraW05ftcaNohiUFzi9jKonhHgCeML0skAIcdyiUt24eAOll7UUrlIr87P78B98/sUNZ3yofHauTc3m5j9Ysnyh+aSpe1TfEKACcnNztxw8uN+7Bl3YCyGKu7AtKHbzhqrdfyqq00tKGWu68W8VQpySUlZkXFSl+50luVEUg2ggpNjrYCC2dCXTxVwAIIT4V3FBKR9lbipGmZ9ro8zNtVHmpmJK3XzNhpTyNkv0W4yq3H+uWUdKeeVvohBiHcZtgR1AghAiwLRaEAAkXsd4FuVGsTE4ADQTQoQLIWwxGmZssLJMCgoKCgr1n6rcfzYAY4WRHkCG6YbvJIQxb7sQwgm4BTherM0jpv8/AqwvVn6fEMJOCBGO0aCx0v0Oc3JDrBhIKXVCiGeALRjdRRZJKU9YWSwFBQUFhXrOte4/QoiJpvPzgc0YXRUjMborjjc19wPWmZKiaYAfpJS/mc59BKwSQjwKRAH3mPo7IYRYBUQAOmBSbXokQD2OfCiEeKLUPpGCCWVuKkaZn2ujzM21UeamYpT5uXGot4qBgoKCgoKCwvVzo9gYKCgoKCgoKNQC9U4xEELcJoQ4LYSINEWTalAIIUKEENuFECeFECeEEFNN5Z5CiK1CiLOmvx7F2rxmmq/TQohbrSd97SCEUAsh/hNCbDS9VubGhBDCXQixRghxyvQZulmZHyNCiOdM36njQogfhRD2DXluhBCLhBCJxd3CqzMfQoguQohjpnOzhWlDXsF61CvFoFjoyqFAa+B+U3jJhoQOeEFK2QroAUwyzcGV8JvNgD9MrykVfvM2YJ5pHuszU4HiMVSVubnKF8BvUsqWQAeM89Tg50cIEQRMAbpKKdtiNEK7j4Y9N0swvrfiVGc+vsIYf+ZKSGFLux8qVEK9UgxQQicjpYy7krxDSpmF8Yc9iDocfrM2EUIEA7cD3xYrVuYGEEK4An2BhQBSykIpZTrK/FxBAzgIITSAI0bf8gY7N6YgPamliq9rPkz++65Syj3SaPD2fbE2ClaivikG1wpL2SARQoQBnYB9lAq/CRQPv9mQ5mwW8DKUyLeqzI2RxkASsNi01fKtyfe6wc+PlDIG+BSjW1kcRj/131HmpjTXOx9Bpv+XLlewIvVNMbB6KMm6ghDCGfgJeFZKmVlR1XLK6uWcCSHuABKllAer2qScsno5NyY0QGfgKyllJyAH01LwNWgw82PaKx8BhGPMeOckhHiooibllNXLuaki15oPZZ7qIPVNMbB6KMm6gBDCBqNSsFxKudZUnGBatkPUsfCbtUgvYLgw5kdfAQwUQixDmZsrRAPRUsp9ptdrMCoKyvzAYOCClDJJSqkF1gI9UeamNNc7H9Gm/5cuV7Ai9U0xaPChk00WvQuBk1LKz4udqrPhN2sLKeVrUspgKWUYxs/Gn1LKh1DmBgApZTxwWQhxJdnNIIzR15T5MW4h9BBCOJq+Y4Mw2u8oc1OS65oP03ZDlhCih2lexxZro2AtpJT16sAYlvIMcA6YZm15rPD+e2NcijsKHDYdwwAvjFbCZ01/PYu1mWaar9PAUGu/h1qap/7ARtP/lbm5+n47Av+aPj8/Ax7K/BS913eAUxhj3S8F7Bry3AA/YrS30GJ88n+0OvMBdDXN6TlgDqbAe8phvUOJfKigoKCgoKBQRH3bSlBQUFBQUFCoAYpioKCgoKCgoFCEohgoKCgoKCgoFKEoBgoKCgoKCgpFKIqBgoKCgoKCQhGKYqCgoKCgoKBQhKIYKCjUIsKYFvuCEMLT9NrD9LqRECLgSiro6+jvUyHEQMtIq6Cg0BBRFAMFhVpESnkZY5rZj0xFHwELpJSXgOeBb66zyy+pOJ+BgoKCwnWhBDhSUKhlTLksDgKLgMeBTlLKQiHEeaCVlLJACDEOY/pZNdAW+AywBR4GCoBhUspUU38HgdulMaSxgoKCQo1QVgwUFGoZaUzC8xIwE2P2y0JT/Pg0KWVBsaptgQeAbsAHQK40Zj3cgzGm/BUOYUwQpaCgoFBjFMVAQcE6DMUYZ76t6XUAkFSqznYpZZaUMgnIAH4xlR8DworVS8SYClhBQUGhxiiKgYJCLSOE6AgMAXoAz5nS0+YB9qWqFl89MBR7bQA0xc7Zm9orKCgo1BhFMVBQqEVMqWW/wriFEAXMAD7FmBE0rJrdNseYnU5BQUGhxiiKgYJC7fI4ECWl3Gp6PQ9oiTH17DkhRNPr6cxkyNgUY6pkBQUFhRqjeCUoKNQRhBCjgC5Syjeus01nKeWblpNMQUGhIaGpvIqCgkJtIKVcJ4Twus5mGoyujAoKCgpmQVkxUFBQUFBQUChCsTFQUFBQUFBQKEJRDBQUFBQUFBSKUBQDBQUFBQUFhSIUxUBBQUFBQUGhCEUxUFBQUFBQUCji//m6+/uD7FWXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x203d253cc40>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap(df,'X','Y','Porosity',xmin,xmax,ymin,ymax,pormin,pormax,'Well Data - Porosity','X(m)','Y(m)','Porosity (fraction)',cmap,'locmap_Porosity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look carefully, and you'll notice the the spatial samples are more dense in the high porosity regions and lower in the low porosity regions.  There is preferential sampling.  We cannot use the naive statistics to represent this region.  We have to correct for the clustering of the samples in the high porosity regions. \n",
    "\n",
    "Let's try cell declustering. We can interpret that we will want to minimize the declustering mean and that a cell size of between 100 - 200m is likely a good cell size, this is 'an ocular' estimate of the largest average spacing in the sparsely sampled regions.   \n",
    "\n",
    "Let's check out the declus program reimplimented from GSLIB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.declus(df, xcol, ycol, vcol, iminmax, noff, ncell, cmin, cmax)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.declus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now populate the parameters. The parameters are:\n",
    "\n",
    "* **df** - DataFrame with the spatial dataset\n",
    "* **xcol** - column with the x coordinate\n",
    "* **ycol** - column with the y coordinate\n",
    "* **vcol** - column with the feature value\n",
    "* **iminmax** - if 1 use the cell size that minimizes the declustered mean, if 0 the cell size that maximizes the declustered mean\n",
    "* **noff** - number of cell mesh offsets to average the declustered weights over\n",
    "* **ncell** - number of cell sizes to consider (between the **cmin** and **cmax**)\n",
    "* **cmin** - minimum cell size\n",
    "* **cmax** - maximum cell size\n",
    "\n",
    "We will run a very wide range of cell sizes, from 10m to 2,000m ('cmin' and 'cmax') and take the cell size that minimizes the declustered mean ('iminmax' = 1 minimize, and = 0 maximize). Multiple offsets (number of these is 'noff') uses multiple grid origins and averages the results to remove sensitivity to grid position.  The ncell is the number of cell sizes.\n",
    "\n",
    "The output from this program is:\n",
    "\n",
    "* **wts** - an array with the weigths for each data (they sum to the number of data, 1 indicates nominal weight)\n",
    "* **cell_sizes** - an array with the considered cell sizes\n",
    "* **dmeans** - an array with the declustered mean for each of the **cell_sizes**\n",
    "\n",
    "The **wts** are the declustering weights for the selected (minimizing or maximizing cell size) and the **cell_sizes** and **dmeans** are plotted to build the diagnostic declustered mean vs. cell size plot (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 289 data with:\n",
      "   mean of      0.13474387540138408 \n",
      "   min and max  0.058547873 and 0.228790002\n",
      "   standard dev 0.03767982164385208 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>Wts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "      <td>5.736104</td>\n",
       "      <td>3.064286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>17.211462</td>\n",
       "      <td>1.076608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "      <td>43.724752</td>\n",
       "      <td>0.997239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094414</td>\n",
       "      <td>1.609942</td>\n",
       "      <td>1.165119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.113049</td>\n",
       "      <td>10.886001</td>\n",
       "      <td>1.224164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y  Facies  Porosity       Perm       Wts\n",
       "0  100  900       1  0.115359   5.736104  3.064286\n",
       "1  100  800       1  0.136425  17.211462  1.076608\n",
       "2  100  600       1  0.135810  43.724752  0.997239\n",
       "3  100  500       0  0.094414   1.609942  1.165119\n",
       "4  100  100       0  0.113049  10.886001  1.224164"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wts, cell_sizes, dmeans = geostats.declus(df,'X','Y','Porosity',iminmax = 1, noff= 10, ncell=100,cmin=10,cmax=2000)\n",
    "df['Wts'] = wts                            # add weights to the sample data DataFrame\n",
    "df.head()                                  # preview to check the sample data DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the location map of the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x203d2c91b50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap(df,'X','Y','Wts',xmin,xmax,ymin,ymax,0.5,2.5,'Well Data Weights','X(m)','Y(m)','Weights',cmap,'locmap_Weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does it look correct?  See the weight varies with local sampling density?  \n",
    "\n",
    "Now let's add the distribution of the weights and the naive and declustered porosity distributions. You should see the histogram bars adjusted by the weights. Also note the change in the mean due to the weights. There is a significant change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porosity naive mean is 0.135.\n",
      "Porosity declustered mean is 0.121.\n",
      "Correction of 0.1002.\n",
      "\n",
      "Summary statistics of the declustering weights:\n",
      "DescribeResult(nobs=289, minmax=(0.2819756671865989, 3.984325446814365), mean=0.9999999999999996, variance=0.40927076480664726, skewness=1.9395015754905447, kurtosis=4.287460624778004)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "GSLIB.locmap_st(df,'X','Y','Wts',xmin,xmax,ymin,ymax,0.0,2.0,'Declustering Weights','X (m)','Y (m)','Weights',cmap)\n",
    "\n",
    "plt.subplot(222)\n",
    "GSLIB.hist_st(df['Wts'],0.5,2.5,log=False,cumul=False,bins=20,weights=None,xlabel=\"Weights\",title=\"Declustering Weights\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "plt.subplot(223)\n",
    "GSLIB.hist_st(df['Porosity'],0.05,0.25,log=False,cumul=False,bins=20,weights=None,xlabel=\"Porosity\",title=\"Naive Porosity\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "plt.subplot(224)\n",
    "GSLIB.hist_st(df['Porosity'],0.05,0.25,log=False,cumul=False,bins=20,weights=df['Wts'],xlabel=\"Porosity\",title=\"Declustered Porosity\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "por_mean = np.average(df['Porosity'].values)\n",
    "por_dmean = np.average(df['Porosity'].values,weights=df['Wts'].values)\n",
    "\n",
    "print('Porosity naive mean is ' + str(round(por_mean,3))+'.')\n",
    "print('Porosity declustered mean is ' + str(round(por_dmean,3))+'.')\n",
    "cor = (por_mean-por_dmean)/por_mean\n",
    "print('Correction of ' + str(round(cor,4)) +'.')\n",
    "\n",
    "print('\\nSummary statistics of the declustering weights:')\n",
    "print(stats.describe(wts))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the plot of the declustered porosity mean vs. the declustering cell size over the 100 runs. At very small and very large cell size the declustered mean is the naive mean. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "plt.scatter(cell_sizes,dmeans, s=30, alpha = 0.8, edgecolors = \"black\", facecolors = 'darkorange')\n",
    "plt.xlabel('Cell Size (m)')\n",
    "plt.ylabel('Declustered Porosity Mean (fraction)')\n",
    "plt.title('Declustered Porosity Mean vs. Cell Size')\n",
    "plt.plot([0,2000],[por_mean,por_mean],color = 'black')\n",
    "plt.plot([200,200],[0.10,0.16],color = 'black',linestyle='dashed')\n",
    "plt.text(300., 0.136, r'Naive Porosity Mean')\n",
    "plt.text(500., 0.118, r'Declustered Porosity Mean')\n",
    "plt.text(230., 0.154, r'Minimizing')\n",
    "plt.text(230., 0.150, r'Cell Size')\n",
    "plt.ylim(0.10,0.16)\n",
    "plt.xlim(0,2000)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell size that minimizes the declustered mean is about 200m (estimated from the figure). This makes sense given our previous observation of the data spacing. \n",
    "\n",
    "#### Calculating Decustered Statistics\n",
    "\n",
    "There are methods available to calculate weighted means, histograms and CDFs. We can always calculate weighted statistics manually.\n",
    "\n",
    "**Weighted Variance**\n",
    "\n",
    "\\begin{equation}\n",
    "\\mu = \\frac{1}{\\sum^{n}_{j=1} w(\\bf{u}_j)} \\sum^{n}_{j=1} w(\\bf{u}_j)x(\\bf{u}_j)\n",
    "\\end{equation}\n",
    "\n",
    "**Weighted Variance**\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2 = \\frac{1}{\\sum^{n}_{j=1} w(\\bf{u}_j)} \\sum^{n}_{j=1} w(\\bf{u}_j)(x(\\bf{u}_j)-\\overline{x})^2\n",
    "\\end{equation}\n",
    "\n",
    "**Weighted Standard Deviation**\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma = \\sqrt{ \\frac{1}{\\sum^{n}_{j=1} w(\\bf{u}_j)} \\sum^{n}_{j=1} w(\\bf{u}_j)(x(\\bf{u}_j)-\\overline{x})^2 }\n",
    "\\end{equation}\n",
    "\n",
    "A convenient way to do this is with the StatsModels' DescrStatsW object that adds the weights to the data (1D or 2D) and then provides member functions for weighted statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Declustered, Weighted Statistics:\n",
      "  Mean: 0.121\n",
      "  Standard Deviation: 0.032\n",
      "  Variance: 0.00102\n"
     ]
    }
   ],
   "source": [
    "weighted_data = DescrStatsW(df['Porosity'].values, weights=df['Wts'], ddof=0)\n",
    "\n",
    "print('Declustered, Weighted Statistics:')\n",
    "print('  Mean: ' + str(round(weighted_data.mean,3)))\n",
    "print('  Standard Deviation: ' + str(round(weighted_data.std,3)))\n",
    "print('  Variance: ' + str(round(weighted_data.var,5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of declustering to correct for sampling bias. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays and many other workflows availble at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy.\n",
    "\n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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